Fixed issue 11963

[mansibrahman03]

What does this PR do?

This PR adds a new expectation, ExpectColumnTypeToBe, that checks a column's declared data type at the schema level. It works the same way across every backend — pandas, SQL databases, and Spark — and always returns a simple result of just {"observed_value": ""}, with no row-level details. It reuses the existing type-matching logic from ExpectColumnValuesToBeOfType and the table.column_types metric. Unit and integration tests were added to confirm expected behavior. This PR does not change the implementation for ExpectColumnValuesToBeOfType.

Why was this PR needed?

The existing ExpectColumnValuesToBeOfType is inconsistent across different datasets, validating at row-level for some datasets and schema-level for other datasets. Reproducing the issue confirmed this inconsistent behavior. The purpose of this PR is to include a separate ExpectColumnTypeToBe expectation that always does a schema-level check consistently everywhere, while ExpectColumnValuesToBeOfType is intended to eventually do only row-level checks.

Relevant issue number

Closes #11963

Screenshots / Recordings

Unit Tests:

Relevant Integration Test:

Does this PR meet the acceptance criteria?

• Tests added for new/changed behavior
• All tests passing
• Follows project style guide
• No breaking changes introduced

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[mansibrahman03]

Hi @joshua-stauffer — this is my first contribution to this project. I've fixed issue #11963 by implementing a new expectation, ExpectColumnTypeToBe, that only checks a column's declared data type at the schema level. Would appreciate a review when you have time!

[mansibrahman03]

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[joshua-stauffer]

hey @mansibrahman03, thanks for the PR! Looks like we're missing a CLA signature for you (see docs here). Once you submit the CLA, comment @cla-bot check to re-run verification. Thanks!