Refactor LongT5 to use @capture_outputs and @can_return_tuple decorators for unified output handling (Fixes #43979)

[cogniera]

What does this PR do?

This PR refactors the LongT5 model to use the @capture_outputs and @can_return_tuple decorators for standardized output handling across the model stack.

The refactor removes manual handling of:

output_attentions

output_hidden_states

return_dict

manual tuple construction

explicit attention/hidden state collection loops

and aligns LongT5 with the modern decorator-based tracing pattern now used across the library.

Fixes (issue)
#43979

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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• Did you write any new necessary tests?

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[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: longt5

[cogniera]

@molbap hi i am new to contributing , I can't really figure out what I did wrong I followed the steps in the repo and the checks are still failing could you please guide me