### 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)

"""
    Tests for DatabricksHook.
    """
    @provide_session
    def setup_method(self, method, session=None):
        conn = session.query(Connection).filter(Connection.conn_id == DEFAULT_CONN_ID).first()
        conn.host = HOST
        conn.login = LOGIN
        conn.password = PASSWORD
        conn.extra = None
        session.commit()
        self.hook = DatabricksHook(retry_delay=0)
    def test_user_agent_string(self):
        op = "DatabricksSql"
        hook = DatabricksHook(retry_delay=0, caller=op)
        ua_string = hook.user_agent_value
        assert ua_string.endswith(f" operator/{op}")
    def test_parse_host_with_proper_host(self):
        host = self.hook._parse_host(HOST)
        assert host == HOST
    def test_parse_host_with_scheme(self):