[Python] Vertex AI Feature Store enrichment handler

[riteshghorse]

This PR adds support for Vertex AI Feature Store with Bigtable online serving and legacy feature store.
It builds on top of the #30001 that added the handler for Bigtable.

Example:

handler = VertexAIFeatureStoreEnrichmentHandler(project=PROJECT_ID,
                                                location=STORE_INSTANCE_REGION,
                                                api_endpoint=ENDPOINT,
                                                feature_store_name=STORE_NAME,
                                                feature_view_name=VIEW_NAME,
                                                row_key=KEY_IN_INPUT_ROW_WITH_UNIQUE_ID,                                                                                     
                                                )

with beam.Pipeline() as p:
  _ = (p
       | "Create Data" >> beam.Create(data)
       |  ... transforms ...
       | "Enrich Data" >> Enrichment(handler)
       | ... transforms ...
      )

The Optimized Serving and Vector Retrieval are in preview launch from Vertex AI which uses the aiplatform_v1beta version of library. Once these are launched from beta, I'll update the client to use that library.

Nothing should change for the user-interface but just the internal of handler where we set up and call the client. I tried this with beta library and it works but I would prefer to not depend on beta library at this point. We will need a different handler for Vector Retrieval case but Optimized Serving can be covered with the existing VertexAIFeatureStoreEnrichmentHandler.

Part of #30386

---

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

• Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
• Update CHANGES.md with noteworthy changes.
• If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI or the workflows README to see a list of phrases to trigger workflows.

[github-actions]

Checks are failing. Will not request review until checks are succeeding. If you'd like to override that behavior, comment assign set of reviewers

[riteshghorse]

assign set of reviewers

[github-actions]

Assigning reviewers. If you would like to opt out of this review, comment assign to next reviewer:

R: @AnandInguva for label python.
R: @Abacn for label build.

Available commands:

• stop reviewer notifications - opt out of the automated review tooling
• remind me after tests pass - tag the comment author after tests pass
• waiting on author - shift the attention set back to the author (any comment or push by the author will return the attention set to the reviewers)

The PR bot will only process comments in the main thread (not review comments).

[riteshghorse]

Failing tests are unrelated to this change

Looking at the other test failures in the meantime

[riteshghorse]

The issue with Python coverage is due to mpmath new version - #30418

Postcommits failed because of unrelated BigQuery IO tests - #30437

[riteshghorse]

R: @damccorm / @tvalentyn (since Anand and Jack are OOO). Let's get it in for 2.55.0

[damccorm]

Looks like test failure is unrelated, so I'm good to merge as long as we have an issue to track the open comment

[riteshghorse]

Unrelated failure/flake. Merging this.