[fix](cloud) use snapshot read for table version to avoid txn conflict (#64647)

[deardeng]

pick from #64647

PR #60467 added a non-snapshot txn->get(table_version_key) in commit_txn, commit_partition, commit_index and drop_partition to return table_version+1 to FE as a version-cache hint. A non-snapshot read adds table_version_key to the transaction's read-conflict range, while update_table_version() bumps the same key via atomic_add (otherwise conflict-free). As a result, concurrent commits/imports on the same table now conflict on the version key and fail with KV_TXN_CONFLICT, causing retries and throughput regression.

Change these reads to snapshot reads. The returned value is only a hint for FE's version cache (reconciled by
CloudTableAndPartitionVersionChecker) and the real increment is still done by atomic_add, so correctness is preserved while the read-conflict on the version key is removed.

(cherry picked from commit 64df172)

What problem does this PR solve?

Issue Number: close #xxx

Related PR: #xxx

Problem Summary:

Release note

None

Check List (For Author)

• Test

  • Regression test
  • Unit Test
  • Manual test (add detailed scripts or steps below)
  • No need to test or manual test. Explain why:
    • This is a refactor/code format and no logic has been changed.
    • Previous test can cover this change.
    • No code files have been changed.
    • Other reason

• Behavior changed:

  • No.
  • Yes.

• Does this need documentation?

  • No.
  • Yes.

Check List (For Reviewer who merge this PR)

• Confirm the release note
• Confirm test cases
• Confirm document
• Add branch pick label

[deardeng]

run buildall

[hello-stephen]

Thank you for your contribution to Apache Doris.
Don't know what should be done next? See How to process your PR.

Please clearly describe your PR:

1. What problem was fixed (it's best to include specific error reporting information). How it was fixed.
2. Which behaviors were modified. What was the previous behavior, what is it now, why was it modified, and what possible impacts might there be.
3. What features were added. Why was this function added?
4. Which code was refactored and why was this part of the code refactored?
5. Which functions were optimized and what is the difference before and after the optimization?