iceberg is not a valid Spark SQL Data Source

### Describe the bug
While running a simple model that select from parquet table create iceberg table I get:
`Caused by: org.apache.spark.sql.AnalysisException: iceberg is not a valid Spark SQL Data Source.`

I tried to follow https://github.com/aws-samples/dbt-glue/issues/405 and https://github.com/apache/iceberg/issues/1756 but without luck.

### Steps To Reproduce
profile.yml
```
glue_dbt:
target: dev
outputs:
dev:
type: glue
role_arn:
region: us-east-1
glue_version: "5.0"
workers: 2
worker_type: G.1X
threads: 50
schema: "iceberg_dev"
idle_timeout: 2
session_provisioning_timeout_in_seconds: 120
location: "s3:///lake/test_schema"
custom_iceberg_catalog_namespace : ""
extra_jars: s3:///jars/iceberg-aws-bundle-1.7.0.jar,s3:///jars/iceberg-spark-runtime-3.3_2.12-1.7.0.jar
conf: |
spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog
--conf spark.sql.catalog.spark_catalog.warehouse=s3:///lake/test_schema
--conf spark.sql.catalog.spark_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog
--conf spark.sql.catalog.spark_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtension
```

dbt_project.yml
```
name: 'glue_dbt'
version: '1.0.0'

profile: 'glue_dbt'

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:
glue_dbt:
materialized: table
```

model/glue/dbt_tracks.sql
```
{{ config(
materialized='incremental',
incremental_strategy='append',
unique_key=['name'],
file_format='iceberg',
iceberg_expire_snapshots='False',
table_properties={'write.target-file-size-bytes': '268435456'}
) }}
WITH
parquet_tracks AS (
SELECT DISTINCT
id,
name
FROM iceberg_dev.stg_tracks
LIMIT 1
)

SELECT *
FROM parquet_tracks
```

### Expected behavior
dbt run works and create the iceberg table with the data.

### Screenshots and log output
```
(glue-dbt) glue_dbt % dbt run
07:30:35 Running with dbt=1.9.1
07:30:35 Registered adapter: glue=1.9.0
07:30:35 Unable to do partial parsing because profile has changed
07:30:36 Found 1 model, 5 data tests, 515 macros
07:30:36
07:30:36 Concurrency: 50 threads (target='dev')
07:30:36
07:30:37 1 of 1 START sql incremental model iceberg_dev.dbt_tracks .............. [RUN]
07:30:38 Glue adapter: Parameter validation failed:
Invalid type for parameter Name, value: , type: , valid types:
07:32:39 Glue adapter: Glue returned `error` for statement None for code SqlWrapper2.execute('''/* {"app": "dbt", "dbt_version": "1.9.1", "profile_name": "glue_dbt", "target_name": "dev", "node_id": "model.glue_dbt.dbt_tracks"} */

insert into table iceberg_dev.dbt_tracks
select id, name from dbt_tracks_tmp
''', use_arrow=False, location='s3:///lake/test_schema'), Py4JJavaError: An error occurred while calling o241.sql.
: java.util.concurrent.ExecutionException: org.apache.spark.sql.AnalysisException: iceberg is not a valid Spark SQL Data Source.
at org.sparkproject.guava.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
at org.sparkproject.guava.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
at org.sparkproject.guava.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
at org.sparkproject.guava.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
at org.sparkproject.guava.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
at org.sparkproject.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
at org.sparkproject.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at org.sparkproject.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
at org.sparkproject.guava.cache.LocalCache.get(LocalCache.java:4000)
at org.sparkproject.guava.cache.LocalCache$LocalManualCache.get(LocalCache.java:4789)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.getCachedPlan(SessionCatalog.scala:212)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.org$apache$spark$sql$execution$datasources$FindDataSourceTable$$readDataSourceTable(DataSourceStrategy.scala:277)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anonfun$apply$2.applyOrElse(DataSourceStrategy.scala:332)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anonfun$apply$2.applyOrElse(DataSourceStrategy.scala:329)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$2(AnalysisHelper.scala:199)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:77)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$1(AnalysisHelper.scala:199)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:353)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning(AnalysisHelper.scala:197)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning$(AnalysisHelper.scala:193)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDownWithPruning(LogicalPlan.scala:34)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning(AnalysisHelper.scala:128)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning$(AnalysisHelper.scala:125)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsWithPruning(LogicalPlan.scala:34)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:85)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:84)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:34)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.apply(DataSourceStrategy.scala:329)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.apply(DataSourceStrategy.scala:249)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:239)
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:236)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$6(RuleExecutor.scala:319)
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:368)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5(RuleExecutor.scala:319)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5$adapted(RuleExecutor.scala:309)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:309)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:195)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:191)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeSameContext(Analyzer.scala:303)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$2(Analyzer.scala:299)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:216)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:299)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:245)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:108)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:270)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:360)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:269)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:93)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:219)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:277)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:711)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:277)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:901)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:276)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:93)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:90)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:82)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:102)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:901)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:99)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:639)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:901)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:630)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:660)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:569)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
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.base/java.lang.Thread.run(Thread.java:840)
Caused by: org.apache.spark.sql.AnalysisException: iceberg is not a valid Spark SQL Data Source.
at org.apache.spark.sql.errors.QueryCompilationErrors$.invalidDataSourceError(QueryCompilationErrors.scala:1542)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:436)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:345)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anon$1.call(DataSourceStrategy.scala:268)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anon$1.call(DataSourceStrategy.scala:255)
at org.sparkproject.guava.cache.LocalCache$LocalManualCache$1.load(LocalCache.java:4792)
at org.sparkproject.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at org.sparkproject.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379)
... 76 more

07:32:39 1 of 1 ERROR creating sql incremental model iceberg_dev.dbt_tracks ..... [ERROR in 121.93s]
07:32:40
07:32:40 Finished running 1 incremental model in 0 hours 2 minutes and 4.19 seconds (124.19s).
07:32:40
07:32:40 Completed with 1 error, 0 partial successes, and 0 warnings:
07:32:40
07:32:40 Database Error in model dbt_tracks (models/glue/dbt_tracks.sql)
```

### System information
**The output of `dbt --version`:**

```
Core:
- installed: 1.9.1
- latest: 1.9.1 - Up to date!

Plugins:
- glue: 1.9.0 - Up to date!
- redshift: 1.9.0 - Up to date!
- postgres: 1.9.0 - Up to date!
- spark: 1.9.0 - Up to date!```
```

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

```
ProductName: macOS
ProductVersion: 15.1.1
BuildVersion: 24B91
```

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

`Python 3.13.1`

### Additional context
I tried a few variations of profile.yml. including:
glue 4/5
with/without:
- `extra_jars`
- `spark.sql.catalog.glue_catalog`
- `spark.sql.defaultCatalog=glue_catalog`
- `datalake_formats`

parquet table is an external table based on json:

```
ROW FORMAT SERDE "org.apache.hive.hcatalog.data.JsonSerDe"
WITH SERDEPROPERTIES (
'ignore.malformed.json' = 'true'
)
LOCATION ''
TBLPROPERTIES (
'projection.enabled' = 'true',
'json.max.read.errors' = '100',
'compression.type' = 'GZIP' ,
'projection.enabled' = 'true',
'projection.date.type' = 'date',
'projection.date.interval' = '1',
'projection.date.format' = 'yyyy/MM/dd',
'timestamp.formats' = "yyyyMMdd'T'HH:mm:ss",
'projection.date.unit' = 'DAYS',
'projection.date.range' = '2021/01/01,NOW',
'storage.location.template' = '/${date}/'
);
```

Start by reproducing `dbt run` with the `profile.yml` and `dbt_project.yml` configuration, using `model/glue/dbt_tracks.sql` as the entry point. Inspect the generated insert and the logged Spark SQL data-source error, then verify that the model creates the requested Iceberg table successfully without the analysis exception.

Written by the indexing model from the issue text.