Iceberg datalake_format can't handle non-iceberg sources

### Describe the bug
When trying to use `{{ source() }}` from a non-iceberg table, a `dbt-glue` project set up for the iceberg datalake format will refuse to read the source because it's not an iceberg table. The table is available in the Glue Data Catalog but the underlying files are JSON. The full logs are below but the main error is

```
org.apache.iceberg.exceptions.ValidationException: Input Glue table is not an iceberg table: glue_catalog.ezmaf_osh.org_smc_maf_events_mafconsentevent (type=null)
```

Everything worked smoothly before I tried to add Iceberg into the equation. Was able to read the source data from JSON and write it to Parquet, just not Iceberg.

### Steps To Reproduce

**profiles.yaml**
```yaml
dev:
target: dev
outputs:
dev:
type: glue
aws_profile_name: dev
query-comment: from dbt
role_arn: "arn:aws:iam::XXXXXXXXXXXX:role/My-Glue-ServiceRole"
region: us-east-1
glue_version: "4.0"
workers: 2
worker_type: G.1X
schema: dbt_glue
idle_timeout: 2
session_provisioning_timeout_in_seconds: 120
location: "s3://my-bucket/dbt-glue"
datalake_formats: iceberg
connections: Iceberg_Glue_4_Connector
conf: >
spark.sql.legacy.timeParserPolicy=LEGACY
--conf spark.sql.legacy.allowNonEmptyLocationInCTAS=true
--conf spark.serializer=org.apache.spark.serializer.JavaSerializer
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
--conf spark.sql.defaultCatalog=glue_catalog
--conf spark.sql.catalog.glue_catalog=org.apache.iceberg.spark.SparkCatalog
--conf spark.sql.catalog.glue_catalog.warehouse=s3://my-bucket/dbt-glue
--conf spark.sql.catalog.glue_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog
--conf spark.sql.catalog.glue_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO
default_arguments: >
--enable-metrics=true,
--enable-continuous-cloudwatch-log=true,
--enable-continuous-log-filter=true,
--enable-spark-ui=true,
--spark-event-logs-path=s3://aws-glue-assets-XXXXXXXXXXXX-us-east-1/sparkUiLogs/dbt/,
--enable-auto-scaling=true
threads: 50
glue_session_reuse: true
```

**sources.yaml**
```yaml
version: 2
sources:
- name: maf
database: glue_catalog
schema: "ezmaf_osh"
tables:
- name: "org_smc_maf_events_mafconsentevent"
```

**dbt_project.yaml**
```yaml
# For more info about what belongs in this file, check the docs https://docs.getdbt.com/reference/dbt_project.yml
name: 'analytics'
version: '2.0.0-pre'
config-version: 2

# This setting configures which "profile" dbt uses for this project.
profile: 'remote'

model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]

clean-targets: # directories to be removed by `dbt clean`
- "target"
- "dbt_packages"

models:
analytics:
+file_format: iceberg
+incremental_strategy: merge
+on_schema_change: sync_all_columns
+persist_docs:
relation: true
columns: true

data_tests:
+severity: warn
```

**models/load/maf/load_maf_consent.sql**
```jinja-sql
{{
config(
materialized="table",
partitioned_by=["day(consentdatetime)"],
tags=["load", "maf"],
)
}}
select
eventname,
cast(consentdatetime as timestamp) consentdatetime,
sessionid,
useridentifier,
username
from
{{
source(
"maf",
"org_smc_maf_events_mafconsentevent",
)
}}
```

### Expected behavior
`dbt-glue` should be able to read from any table accessible via the Glue Data Catalog and write back to S3 in Iceberg format.

### Screenshots and log output

```
07:35:17 Running with dbt=1.8.1
07:35:17 Registered adapter: glue=1.8.1
07:35:17 Unable to do partial parsing because of a version mismatch
07:35:19 Found 58 models, 6 seeds, 415 data tests, 32 sources, 2 exposures, 620 macros
07:35:19
07:35:20 Concurrency: 50 threads (target='dev')
07:35:20
07:35:20 1 of 1 START sql table model dbt_glue.load_maf_consent ......................... [RUN]
07:37:55 Glue adapter: Glue returned `error` for statement None for code SqlWrapper2.execute('''/* {"app": "dbt", "dbt_version": "1.8.1", "profile_name": "dev", "target_name": "dev", "node_id": "model.analytics.load_maf_consent"} */

create table dbt_glue.load_maf_consent



using iceberg

LOCATION 's3://my-bucket/dbt-glue/dbt_glue/load_maf_consent'
comment 'Event emitted when the parent initially consents to using the ezMAF app. At the moment, this should only happen once per `useridentifier` but there are future plans to make this happen once per `uniqueformid`'

as

select
eventname,
cast(consentdatetime as timestamp) consentdatetime,
sessionid,
useridentifier,
username
from
ezmaf_osh.org_smc_maf_events_mafconsentevent
''', use_arrow=False, location='s3://my-bucket/dbt-glue'), Py4JJavaError: An error occurred while calling o105.sql.
: org.apache.iceberg.exceptions.ValidationException: Input Glue table is not an iceberg table: glue_catalog.ezmaf_osh.org_smc_maf_events_mafconsentevent (type=null)
at org.apache.iceberg.exceptions.ValidationException.check(ValidationException.java:49)
at org.apache.iceberg.aws.glue.GlueToIcebergConverter.validateTable(GlueToIcebergConverter.java:48)
at org.apache.iceberg.aws.glue.GlueTableOperations.doRefresh(GlueTableOperations.java:116)
at org.apache.iceberg.BaseMetastoreTableOperations.refresh(BaseMetastoreTableOperations.java:95)
at org.apache.iceberg.BaseMetastoreTableOperations.current(BaseMetastoreTableOperations.java:78)
at org.apache.iceberg.BaseMetastoreCatalog.loadTable(BaseMetastoreCatalog.java:43)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.lambda$doComputeIfAbsent$14(BoundedLocalCache.java:2406)
at java.util.concurrent.ConcurrentHashMap.compute(ConcurrentHashMap.java:1853)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.doComputeIfAbsent(BoundedLocalCache.java:2404)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.BoundedLocalCache.computeIfAbsent(BoundedLocalCache.java:2387)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.LocalCache.computeIfAbsent(LocalCache.java:108)
at org.apache.iceberg.shaded.com.github.benmanes.caffeine.cache.LocalManualCache.get(LocalManualCache.java:62)
at org.apache.iceberg.CachingCatalog.loadTable(CachingCatalog.java:166)
at org.apache.iceberg.spark.SparkCatalog.load(SparkCatalog.java:587)
at org.apache.iceberg.spark.SparkCatalog.loadTable(SparkCatalog.java:142)
at org.apache.iceberg.spark.SparkCatalog.loadTable(SparkCatalog.java:99)
at org.apache.spark.sql.connector.catalog.CatalogV2Util$.loadTable(CatalogV2Util.scala:311)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupRelation$3(Analyzer.scala:1206)
at scala.Option.orElse(Option.scala:447)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupRelation$1(Analyzer.scala:1205)
at scala.Option.orElse(Option.scala:447)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupRelation(Analyzer.scala:1197)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1068)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1032)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:177)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$2(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1249)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1248)
at org.apache.spark.sql.catalyst.plans.logical.OrderPreservingUnaryNode.mapChildren(LogicalPlan.scala:226)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$2(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.trees.BinaryLike.mapChildren(TreeNode.scala:1277)
at org.apache.spark.sql.catalyst.trees.BinaryLike.mapChildren$(TreeNode.scala:1274)
at org.apache.spark.sql.catalyst.plans.logical.CreateTableAsSelect.mapChildren(v2Commands.scala:298)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1032)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:991)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:215)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:91)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeBatch$1(RuleExecutor.scala:212)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$6(RuleExecutor.scala:284)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$RuleExecutionContext$.withContext(RuleExecutor.scala:327)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5(RuleExecutor.scala:284)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5$adapted(RuleExecutor.scala:274)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:274)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:188)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:227)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:223)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:172)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:223)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:187)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:208)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:207)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:78)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:192)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:213)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:552)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:213)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:212)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:78)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:76)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:68)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:101)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:99)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:622)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:617)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:750)

07:37:55 1 of 1 ERROR creating sql table model dbt_glue.load_maf_consent ................ [ERROR in 155.13s]
07:37:55
07:37:55 Finished running 1 table model in 0 hours 2 minutes and 36.69 seconds (156.69s).
07:37:55
07:37:55 Completed with 1 error and 0 warnings:
07:37:55
07:37:55 Runtime Error in model load_maf_consent (models/load/maf/load_maf_consent.sql)
module 'dbt.exceptions' has no attribute 'DbtDatabaseError'
07:37:55
07:37:55 Done. PASS=0 WARN=0 ERROR=1 SKIP=0 TOTAL=1
```

### System information
**The output of `dbt --version`:**
```
Core:
- installed: 1.8.1
- latest: 1.8.3 - Update available!

Your version of dbt-core is out of date!
You can find instructions for upgrading here:
https://docs.getdbt.com/docs/installation

Plugins:
- glue: 1.8.1 - Up to date!
- spark: 1.8.0 - Up to date!
- athena: 1.8.2 - Ahead of latest version!
```

**The operating system you're using:**
Mac OS

**The output of `python --version`:**
Python 3.12.4

### Additional context

Nothing at the moment

Start with the generated CREATE TABLE AS SELECT shown in the logs, then reproduce it using profiles.yaml, sources.yaml, dbt_project.yaml, and models/load/maf/load_maf_consent.sql. Trace how the Iceberg catalog configuration resolves the non-Iceberg Glue source. Done means a source backed by JSON can be read through the Glue Data Catalog while the model is written in Iceberg format.

Written by the indexing model from the issue text.