Add "interval": "validation" to scheduler configuration

Nobody has claimed this yet.

## 🚀 Feature

This request concerns the optimizer Scheduler.

For now, the scheduler can be configured like this:
```python
lr_scheduler_config = {
"scheduler": lr_scheduler,
"interval": "epoch", # epoch, step
"frequency": 1,
"monitor": "val_loss",
"strict": True,
"name": None,
}
```

It would be great to add the option `"interval": "validation"`.

### Motivation

My use case is the following. I want to `.step()` the scheduler after each evaluation step, using an evaluation metric and `ReduceLROnPlateau` scheduler.

Sometimes **(1)** I'm using a bigger dataset, so in the case I use `val_check_interval=0.1`.
and Other times **(2)** I'm using smaller dataset, so I use `check_val_every_n_epoch=5`.

The current interval options are not working for me. `step` will call the scheduler each step, and `epoch` will do it after each epoch, however, for my case **(1)**, the scheduler will be called once every 10 validations, and in **(2)**, the scheduler will be called before the validation, and I will get the error `pytorch_lightning.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric my_quantity which is not available. Available metrics are: ['train/loss']. Condition can be set using 'monitor' key in lr scheduler dict`.

I hope this is clear for you.

cc @borda @tchaton @rohitgr7

No files or tests are named. Start at the optimizer Scheduler configuration and trace how interval, val_check_interval, and check_val_every_n_epoch control stepping, especially for ReduceLROnPlateau. Done means a validation interval triggers stepping for both example setups and the monitored metric is available.

Written by the indexing model from the issue text.