feat: add should_complete to CompletionConfig

[ayushiahjolia]

Issue #, if available: #519

Description of changes:
Adds a should_complete predicate to CompletionConfig for map and parallel operations, giving full control over when a batch completes early.

from aws_durable_execution_sdk_python import complete_batch, continue_batch

config = CompletionConfig(
    should_complete=lambda status: (
        complete_batch() if status.success_count >= 2
        else continue_batch()
    )
)

The predicate receives a CompletionStatus snapshot (counts + per-item statuses) and returns a CompletionDecision - either continue_batch() or complete_batch(outcome). The outcome determines whether CUSTOM_COMPLETION_SUCCEEDED or CUSTOM_COMPLETION_FAILED is reported.

Key design decisions -

• Cannot be combined with threshold fields (raises ValidationError)
• Predicate must be deterministic and side-effect-free
• Replay uses the checkpointed decision, never re-invokes the predicate
• On resumed invocations, deferred until prior checkpoints replay

Testing -

• Unit tests, integration tests and examples

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[github-actions]

Codex AI review

Found two correctness issues in custom predicate scheduling. Resume ordering and STARTED-state evaluation remain inadequately covered.

Reviewed commit 65d2fa02e297e3cc222e68ee830068c208ba9e7d. Workflow run