### 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)

"""
        Obtains Repos ID by path
        :param path: path to a repository
        :return: Repos ID if it exists, None if doesn't.
        """
        try:
            result = self._do_api_call(WORKSPACE_GET_STATUS_ENDPOINT, {"path": path}, wrap_http_errors=False)
            if result.get("object_type", "") == "REPO":
                return str(result["object_id"])
        except requests_exceptions.HTTPError as e:
            if e.response.status_code != 404:
                raise e
        return None
    def test_connection(self) -> tuple[bool, str]:
        """Test the Databricks connectivity from UI"""
        hook = DatabricksHook(databricks_conn_id=self.databricks_conn_id)
        try:
            hook._do_api_call(endpoint_info=SPARK_VERSIONS_ENDPOINT).get("versions")
            status = True
            message = "Connection successfully tested"
        except Exception as e:
            status = False
            message = str(e)
        return status, message