min_epochs and EarlyStopping in conflict

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### Bug description

I have a problem where I use `min_epochs` because it can take a while before the training starts to converge.
EarlyStopping is triggered quite early, but I thought to set `min_epochs` appropriately to 'get over' that initial period.
However, even though training is converging by the time we reach `min_epochs`, early stopping will stop training immediately once we reached `min_epochs`, just because it was triggered very early on in training.

I think that `EarlyStopping` should pick itself back up if we improve upon the monitored metric before reaching `min_epochs`.

Example `Trainer` config:

```
trainer = L.Trainer(
max_epochs=10000,
callbacks=[
EarlyStopping(monitor="val_loss", mode="min", patience=100),
]
min_epochs=1000,
)
```
Now imagine `EarlyStopping` triggering at epoch 100, but `val_loss` improving at 101 all the way until epoch 1000 - right now training will still stop.

### What version are you seeing the problem on?

v2.2

### How to reproduce the bug

_No response_

### Error messages and logs

_No response_

### Environment

_No response_

### More info

_No response_

Start with Trainer's min_epochs handling and the EarlyStopping callback, using the configuration in the report as a minimal reproduction. Verify that improvement in val_loss before epoch 1000 prevents stopping at min_epochs, and add regression coverage for that behavior.

Written by the indexing model from the issue text.