### 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)

mock_requests.codes.ok = 200
        mock_requests.post.return_value.json.return_value = {}
        status_code_mock = mock.PropertyMock(return_value=200)
        type(mock_requests.post.return_value).status_code = status_code_mock
        data = {"cluster_id": CLUSTER_ID, "libraries": LIBRARIES}
        self.hook.uninstall(data)
        mock_requests.post.assert_called_once_with(
            uninstall_endpoint(HOST),
            json={"cluster_id": CLUSTER_ID, "libraries": LIBRARIES},
            params=None,
            auth=HTTPBasicAuth(LOGIN, PASSWORD),
            headers=self.hook.user_agent_header,
            timeout=self.hook.timeout_seconds,
        )
    def test_is_aad_token_valid_returns_true(self):