[FLINK-38918][python][kafka connector] support per cluster offset for DynamicKafkaSource in pyflink

[bowenli86]

What is the purpose of the change

This PR addresses FLINK-38918 by adding PyFlink support for configuring per-cluster starting and stopping offsets for DynamicKafkaSource.

The Java dynamic Kafka connector already supports offset initializers on ClusterMetadata and SingleClusterTopicMetadataService; this change exposes that capability through the PyFlink wrappers.

Brief change log

• Added a PyFlink ClusterMetadata wrapper that forwards topics, properties, and optional starting/stopping KafkaOffsetsInitializers to the Java ClusterMetadata.
• Extended SingleClusterTopicMetadataService with optional per-cluster starting and stopping offsets while preserving the existing constructor behavior when offsets are omitted.
• Exported ClusterMetadata through pyflink.datastream.connectors.
• Updated the flink-python test Kafka SQL connector dependency to 5.0.0-2.2.
• Added Python unit tests for full, default, starting-only, and stopping-only per-cluster offset configurations.

Verifying this change

This change added tests and can be verified as follows:

• Added unit tests in flink-python/pyflink/datastream/connectors/tests/test_dynamic_kafka.py for SingleClusterTopicMetadataService offset forwarding.
• Added unit tests in flink-python/pyflink/datastream/connectors/tests/test_dynamic_kafka.py for ClusterMetadata offset forwarding.
• Added coverage for backward-compatible default behavior when no per-cluster offsets are provided.
• Added coverage for partial configurations where only starting offsets or only stopping offsets are provided.

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

• Dependencies (does it add or upgrade a dependency): yes, upgrades a flink-python test-scoped Kafka SQL connector dependency
• The public API, i.e., is any changed class annotated with @Public(Evolving): yes, exposes PyFlink connector API additions
• 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? Python API docstrings

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Was generative AI tooling used to co-author this PR?

• Yes (Codex GPT-5)

Generated-by: Codex GPT-5

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CI report:

• b7b3cc7 Azure: SUCCESS

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[bowenli86]

thank you everyone for review, the pr is good to go per offline sync
merging