Experiment manager MlFlow logger does not log model checkpoints as artifacts
**Is your feature request related to a problem? Please describe.**

MlFlow is required for training and resuming runs from checkpoint. Currently, when the MlFlow logger is enabled in experiment manager it doesn't log model checkpoint as artifacts to MLFlow

**Describe the solution you'd like**

One of the parameters (`log_model`) must to be passed to `pytorch_lightning.loggers.MLFlowLogger` is not defined in `nemo.utils.loggers.MLFlowParams` dataclass and cannot be set without modifying the code.

**Describe alternatives you've considered**

Doesn't seem to have an alternative

**Additional context**

Add any other context or screenshots about the feature request here.
