### Description

The Databricks jobs 2.1 API has the ability to repair failed or skipped tasks in a Databricks workflow without having to rerun successful tasks for a given workflow run. It would be nice to be able to leverage this functionality via airflow operators.

### Use case/motivation

The primary motivation is the ability to be more efficient and only have to rerun failed or skipped tasks rather than the entire workflow if only 1 out of 10 tasks fail.

**Repair run API:**
https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsRepairfail

@alexott for visability

### 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)

"""
        Retrieves run output of the run.
        :param run_id: id of the run
        :return: output of the run
        """
        json = {"run_id": run_id}
        run_output = self._do_api_call(OUTPUT_RUNS_JOB_ENDPOINT, json)
        return run_output
    def cancel_run(self, run_id: int) -> None:
        """
        Cancels the run.
        :param run_id: id of the run
        """
        json = {"run_id": run_id}
        self._do_api_call(CANCEL_RUN_ENDPOINT, json)
    def delete_run(self, run_id: int) -> None:
        """
        Deletes a non-active run.
        :param run_id: id of the run
        """
        json = {"run_id": run_id}
        self._do_api_call(DELETE_RUN_ENDPOINT, json)
    def restart_cluster(self, json: dict) -> None:
        """
        Restarts the cluster.
        :param json: json dictionary containing cluster specification.
        """
        self._do_api_call(RESTART_CLUSTER_ENDPOINT, json)
    def start_cluster(self, json: dict) -> None: