Is this a new bug in dbt-spark?

• I believe this is a new bug in dbt-spark
• I have searched the existing issues, and I could not find an existing issue for this bug

Current Behavior

When I define model like following, and execute dbt run command, command will succeed if metadata field is really of type STRUCT, but underlying keys in the struct and their corresponding data types won't be checked.

models:
  - name: test_model
    config:
      contract:
        enforced: true
        alias_types: false
    columns:
      - name: id
        data_type: STRING

      - name: metadata
        data_type: STRUCT<property_1 STRING, property_2 STRING>

SQL model:

  select
    id,
    CAST(metadata AS STRUCT<property_3 STRING> as metadata
  from {{ source('source', 'source_table') }}

I've noticed this when I tried testing and changing my data_type from STRUCT to MAP. In that case dbt run command retuned this error:

definition_type and contract_type are not taking into account values inside complex/nested structures.

Expected Behavior

I would expect for dbt to fail if contract is enforced, and complex data types like ARRAY, MAP and STRUCT are not fully described, or even better under contract configuration to have check_complex_types boolean field which can decide whether to check nested structures or not.

Steps To Reproduce

Using model configuration and similar SQL command (that contains STRUCT field) from above, execute dbt run command. Command will succeed no matter what underlying struct configuration is setup in data_type field (works with setting just STRUCT<>).

Relevant log output

No response

Environment

- OS:
- Python: 3.11.9
- dbt-core: dbt Cloud CLI - 0.38.15
- dbt-spark:

Additional Context

No response