fix: restore cached AMP step context after no_grad workaround

[littlebullGit]

What does this PR do?

Fixes #21611

This PR addresses the AMP memory regression introduced when #20921 changed MixedPrecision.autocast_context_manager() to use
cache_enabled=False globally as a workaround for the nested no_grad() autocast cache-poisoning bug reported in #20644.

That workaround fixed the original correctness issue, but it also forced Lightning's step execution onto the uncached autocast
path. In workloads that call the same module repeatedly within one training_step (for example iterative decoding / RL-style
loops), this can cause repeated recasting of the same parameters and significant memory growth, which is the regression
reported in #21611.

This PR keeps the existing public behavior of MixedPrecision.autocast_context_manager() unchanged for compatibility, but
narrows the workaround in Lightning's internal runtime path:

• autocast_context_manager() still uses cache_enabled=False
• forward_context() now uses the cached autocast path again
• when nested torch.no_grad() or torch.inference_mode() exits inside forward_context(), the autocast cache gets cleared
  so the original Computation graph not being built #20644 bug remains fixed

In short, this restores the cached AMP behavior for normal Lightning step execution while preserving the original nested
no_grad() safeguard.

Changes

• keep MixedPrecision.autocast_context_manager() behavior unchanged
• move the narrower workaround into MixedPrecision.forward_context()
• clear the autocast cache after nested no_grad() / inference_mode() exits within forward_context()
• add regression tests covering:
  • raw PyTorch autocast behavior with cache_enabled=True vs False
  • nested no_grad() inside Lightning AMP forward_context()
  • nested inference_mode() inside Lightning AMP forward_context()
  • restoration of patched grad-mode context managers after exiting forward_context()

---

📚 Documentation preview 📚: https://pytorch-lightning--21616.org.readthedocs.build/en/21616/

[codecov]

Codecov Report

❌ Patch coverage is 96.66667% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 79%. Comparing base (612ab08) to head (3d5a8d6).
✅ All tests successful. No failed tests found.

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HEAD has 168 uploads less than BASE

Flag	BASE (612ab08)	HEAD (3d5a8d6)
cpu	84	42
lightning_fabric	27	0
pytest	42	0
python3.12	24	12
python	6	3
lightning	30	15
python3.11	12	6
python3.13	18	9
python3.12.7	18	9
python3.10	6	3

Additional details and impacted files

@@            Coverage Diff            @@
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- Coverage      87%      79%     -8%     
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  Files         270      267      -3     
  Lines       23898    23866     -32     
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- Hits        20678    18794   -1884     
- Misses       3220     5072   +1852     

[deependujha]

Is there any specific reason for not having CUDA tests? Adding them would be valuable.