add save_last_n_optim_states flag
closes #9967  > I think this approach is correct, although quite inefficient (after a few saves, we will load and save old ckpt on **each** new ckpt save).
> 
> For now I think it's fine for users who need this functionality, long term we can separate the model and optimizer state inside the checkpoint so that it's easy to remove the opt state without load/save.

For a more efficient implementation of dropping optimizer state, I see 2 alternatives:
- One is NeMo specific and described here: https://gitlab-master.nvidia.com/dl/JoC/nemo-ci/-/issues/578#note_24602416
- second option is adding a capability to separate different parts of the checkpoint **within** the `torch_dist` format so that it's easy to drop one of them in-place. This would be implemented inside MCore and NeMo would just specify that `optimizier_states` is the part to separate during saving and drop during cleanup.
# What does this PR do ?

1. Adds save_last_n_optim_states to save optimizer states only for last n checkpoints. This approach allows to save the disk space. Checkpoint file without optimizer states weighs about 7 times less
2. Now it's also possible to load checkpoint without optimizer states for the inference.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* To save optimizer states for last 2 checkpoints, set this config value:

```python
exp_manager.checkpoint_callback_params.save_last_n_optim_states=2 
```
`save_last_n_optim_states=-1` is set by default. It means that optimizer states will be saved for each checkpoint. In order to drop optimizer states for each training checkpoint, you can set `save_last_n_optim_states=0`.

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