### 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)

"""
        Retrieve run information.
        :param run_id: id of the run
        :return: state of the run
        """
        json = {"run_id": run_id}
        response = self._do_api_call(GET_RUN_ENDPOINT, json)
        return response
    async def a_get_run(self, run_id: int) -> dict[str, Any]:
        """
        Async version of `get_run`.
        :param run_id: id of the run
        :return: state of the run
        """
        json = {"run_id": run_id}
        response = await self._a_do_api_call(GET_RUN_ENDPOINT, json)
        return response
    def get_run_state_str(self, run_id: int) -> str:
        """
        Return the string representation of RunState.
        :param run_id: id of the run
        :return: string describing run state
        """
        state = self.get_run_state(run_id)
        run_state_str = (
            f"State: {state.life_cycle_state}. Result: {state.result_state}. {state.state_message}"
        )
        return run_state_str
    def get_run_state_lifecycle(self, run_id: int) -> str: