Change `optimizer` or `lr_scheduler` in resuming training without removing the `global_step` information

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### Description & Motivation

Hello,

In case we want to resume training from a checkpoint, but for say change the `optimizer` class or the `lr_scheduler` class, it seems the `global_step` becomes 0. Is there a possibility to keep the global step information and still allow the change? This would greatly benefit if in case we are using a warmup + decay lr_scheduler, and on resume training, we want to change the number of decay steps. However with this change if the global step becomes 0, the model will do a warmup at resume, which is not intended.

### Pitch

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### Alternatives

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### Additional context

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cc @lantiga @borda

Start by tracing the checkpoint-resume path for optimizer and lr_scheduler state, focusing on where global_step is restored or reset. Done means changing either class while resuming preserves the checkpoint's global_step and avoids an unintended scheduler warmup.

Written by the indexing model from the issue text.