Missing catalog name in CTAS

### Describe the bug
We have a simple DBT model as follows:

```
# model.yaml

version: 2

models:
- name: matches
description: RS
config:
materialized: 'table'
incremental_strategy: 'insert_overwrite'
file_format: iceberg
glue_version: '4.0'
glue_session_reuse: True
iceberg_expire_snapshots: True
custom_location: '{{target.location}}/matches'
columns:
- name: id
type: varchar
- name: rating
type: integer
```

And the very simple `matches.sql` model

```
SELECT * FROM {{ source('matches_source', 'sourcetest') }}
```

However, it fails with

```
23:55:06 Completed with 1 error and 0 warnings:
23:55:06
23:55:06 Database Error in model matches (dbt_models/matches/matches.sql)
Glue cursor returned `error` for statement None for code SqlWrapper2.execute('''/* {"app": "dbt", "dbt_version": "1.7.9", "profile_name": "ml_iceberg", "target_name": "dev", "node_id": "model.ml_dating_outcomes.m
atches"} */

create table dating_outcomes.matches

using iceberg

location 's3://<>/iceberg-tables/dating_outcomes/matches'

as
SELECT * FROM ml_features.sourcetest
''', use_arrow=False, location='s3://<>/iceberg-tables/dating_outcomes'), Py4JJavaError: An error occurred while calling o81.sql.
: org.apache.spark.SparkException: Table implementation does not support writes: dating_outcomes.matches
at org.apache.spark.sql.errors.QueryExecutionErrors$.unsupportedTableWritesError(QueryExecutionErrors.scala:761) at org.apache.spark.sql.execution.datasources.v2.TableWriteExecHelper.$anonfun$writeToTable$1(WriteToDataSourceV2Exec.scala:515)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1550)
at org.apache.spark.sql.execution.datasources.v2.TableWriteExecHelper.writeToTable(WriteToDataSourceV2Exec.scala:491)
at org.apache.spark.sql.execution.datasources.v2.TableWriteExecHelper.writeToTable$(WriteToDataSourceV2Exec.scala:486)
at org.apache.spark.sql.execution.datasources.v2.CreateTableAsSelectExec.writeToTable(WriteToDataSourceV2Exec.scala:68)
at org.apache.spark.sql.execution.datasources.v2.CreateTableAsSelectExec.run(WriteToDataSourceV2Exec.scala:92)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:43)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:43)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:49)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:103)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:224)
at org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:114)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$7(SQLExecution.scala:139)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:224)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:139)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:245)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:138)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:100)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:96)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:615)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:177)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:615)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:591)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:96)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:83)
...
```

I believe this is because the [CTE](https://github.com/aws-samples/dbt-glue/blob/8878a5525636931ea15fe1ca625894a352857cad/dbt/include/glue/macros/adapters.sql#L68) is missing the catalog name (instead of `dating_outcomes.matches`, it should be `glue_catalog.dating_outcomes.matches`), and have confirmed the above works when we add it to the macro by overloading it.

### Steps To Reproduce
In as much detail as possible, please provide steps to reproduce the issue. Sample data that triggers the issue, example model code, etc is all very helpful here.

### Expected behavior
The table should have been created

### Screenshots and log output
If applicable, add screenshots or log output to help explain your problem.

### System information

DBT Profile:

```
config:
partial_parse: False
send_anonymous_usage_stats: False
use_colors: False

ml_iceberg:
target: dev # default target
outputs:
dev:
type: glue
query-comment: Glue on the dev account
role_arn: <>
region: us-east-1
workers: 2
worker_type: G.1X
idle_timeout: 10
schema: <>
database: <>
session_provisioning_timeout_in_seconds: 120
location: s3://<>/iceberg-tables/<>
glue_version: "4.0"
glue_session_id: dbt
datalake_formats: iceberg
conf: |
spark.sql.catalog.glue_catalog=org.apache.iceberg.spark.SparkCatalog
--conf spark.sql.catalog.glue_catalog.warehouse=s3://<>
--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
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
```

glue version: 4
dbt 1.7.9
```

```

### Additional context
Add any other context about the problem here.

Start with dbt/include/glue/macros/adapters.sql at the linked line and inspect how the CTAS statement is assembled. Reproduce the dbt 1.7.9 Iceberg model with the supplied Glue configuration, then verify that the generated table reference uses the configured catalog and that the table is created successfully.

Written by the indexing model from the issue text.