[FLINK-40424][table][python] Support windowing TVFs in the DataFrame API

[Timm0]

What is the purpose of the change

This change adds tumble, hop, cumulate, and session window methods to the DataFrame API and the internal Table API machinery they need. The window operation is built programmatically as a LogicalTableFunctionScan
rather than by generating SQL strings, so the DataFrame layer does not have to track Flink's SQL dialect. No new public Table API is introduced, since all added Table API types are @Internal.

Brief change log

• Add tumble/hop/cumulate/session window methods to the PyFlink DataFrame API, with time-column resolution and day-time interval handling
• Introduce internal WindowTableFunctionQueryOperation
• Dispatch it through QueryOperationVisitor and QueryOperationDefaultVisitor and construct it via OperationTreeBuilder.windowTableFunction
• Convert the operation to a LogicalTableFunctionScan programmatically in QueryOperationConverter
• Add tests: api-java operation test, planner WindowTableFunctionRelNodeTest, QueryOperation serialization + semantic programs, and PyFlink unit + IT tests
• Expose createTable/getOperationTreeBuilder on the Table API test steps and refactor _materialize into test_case_utils for stream test cases
• Document the window methods in dataframe.rst

Verifying this change

• Added api-java operation test
• Added planner WindowTableFunctionRelNodeTest
• Added QueryOperation serialization + semantic programs
• Added PyFlink unit + IT tests

Does this pull request potentially affect one of the following parts:

• Dependencies (does it add or upgrade a dependency): no
• The public API, i.e., is any changed class annotated with @Public(Evolving): yes
• The serializers: no
• The runtime per-record code paths (performance sensitive): no
• Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
• The S3 file system connector: no

Documentation

• Does this pull request introduce a new feature? yes
• If yes, how is the feature documented? docs flink-python/docs/reference/pyflink.dataframe/dataframe.rst + Python docstrings

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CI report:

• ab68303 Azure: FAILURE

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