fsdp: avoid double sharding of MoE experts when EP is enabled

[CodersAcademy006]

When Expert Parallelism (EP) is enabled, MoE expert parameters are already
partitioned across ranks. However, current FSDP auto-wrapping logic still
wraps these expert modules, causing parameters to be effectively tracked
and sharded twice.

This results in:

• Increased peak GPU memory usage
• Duplicated optimizer state
• Additional communication overhead
• Reduced throughput compared to EP-only runs

What this PR does

• Skips FSDP auto-wrapping for MoE expert modules that are already managed
  by Expert Parallelism
• Adds an explicit ownership signal (expert_parallel_enabled) to MoE layers
  to avoid heuristic or name-based checks
• Preserves existing FSDP behavior for all non-expert parameters

Impact

• Restores expected memory behavior (EP + FSDP ≤ EP-only)
• Reduces redundant parameter metadata and optimizer state
• Improves training throughput
• No behavior change for non-MoE or non-EP models

Reproduction

This issue is reproducible on large MoE models and is reported in #2772.
The change removes the redundant sharding path responsible for the memory
regression.

Fixes #2772

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