Error Handling: refactor ExecuteComputation and ExecuteReplicated to propagate status.

[ysiraichi]

This PR refactors both ComputationClient:::ExecuteComputation and ComputationClient:::ExecuteReplicated functions for propagating error statuses.

Key Changes:

• Refactors ExecuteComputation and ExecuteReplicated: Now explicitly propagates absl::Status.
• Replaces Raw .value() Calls: Uses XLA_ASSIGN_OR_RETURN_WITH_LOCATION for safer error handling.
• Updates Call Sites: Leverages GetValueOrThrow for more robust error management.

Example:

# Run this in eager mode + with C++ error context set.
a = torch.rand(1024, 1024, 1024, 1024, 1024, device=device)

Before this PR: no source code location

Traceback (most recent call last):
  File "/home/ysiraichi/ext/examples/mem.py", line 12, in <module>
    a = torch.rand(1024, 1024, 1024, 1024, 1024, device=device)
RuntimeError: Out of memory allocating 4503599627370496 bytes. 

After this PR: source code location of the function call that raised this error

Traceback (most recent call last):
  File "/home/ysiraichi/ext/examples/mem.py", line 12, in <module>
    a = torch.rand(1024, 1024, 1024, 1024, 1024, device=device)
RuntimeError: Out of memory allocating 4503599627370496 bytes. (at torch_xla/csrc/runtime/pjrt_computation_client.cpp:761)

[ysiraichi]

Blocked until #9429 is merged.