### Description

It would be nice if we could give the DatabricksSubmitRunOperator a pipeline name instead of a pipeline_id for cases when you do not already know the pipeline_id but do know the name. I'm not sure if there's an easy way to fetch a pipeline_id.

### Use case/motivation

Avoid hardcoding pipeline ID's, storing the ID's elsewhere, or fetching the pipeline list and filtering it manually if the pipeline name is known, but ID is not.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
    Submits a Spark job run to Databricks using the api/2.1/jobs/runs/submit API endpoint.
    See: https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
    There are three ways to instantiate this operator.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:DatabricksSubmitRunOperator`
    :param tasks: Array of Objects(RunSubmitTaskSettings) <= 100 items.
        .. seealso::
            https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
    :param json: A JSON object containing API parameters which will be passed
        directly to the ``api/2.1/jobs/runs/submit`` endpoint. The other named parameters
        (i.e. ``spark_jar_task``, ``notebook_task``..) to this operator will
        be merged with this json dictionary if they are provided.
        If there are conflicts during the merge, the named parameters will
        take precedence and override the top level json keys. (templated)
        .. seealso::
            For more information about templating see :ref:`concepts:jinja-templating`.
            https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit
    :param spark_jar_task: The main class and parameters for the JAR task. Note that
        the actual JAR is specified in the ``libraries``.
        *EITHER* ``spark_jar_task`` *OR* ``notebook_task`` *OR* ``spark_python_task``