[FEA]: Update DFP example pipeline(s) to support customized experiment and model naming, and make model names Databricks compatible.

[drobison00]

Is this a new feature, an improvement, or a change to existing functionality?

Improvement

How would you describe the priority of this feature request

High

Please provide a clear description of problem this feature solves

• Static Model and Experiment names The current example DFP pipelines have static model_name_formatter and experiment_name_formatter names, which are not compatible in some Databricks environments.
• Invalid model names Databrick's MLflow service will reject model names containing '/', '.', and ':', we also want to update model names in the mlflow stage to preprocess these accordingly.

Describe your ideal solution

Add two new click options to the dfp_xxx_pipeline.py files:

@click.option('--mlflow_experiment_name_template',
              type=str,
              default="dfp/azure/training/{reg_model_name}",
              help="The MLflow experiment name template to use when logging experiments. ")
@click.option('--mlflow_model_name_template',
              type=str,
              default="DFP-azure-{user_id}",
              help="The MLflow model name template to use when logging models. ")

Update model naming, which currently incorporates 'user_id', to replace '.', '/', ':' with 'dot', 'slash', 'colon' respectively.

Additional context

No response

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