### 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)

@provide_session
    def setup_method(self, method, session=None):
        conn = session.query(Connection).filter(Connection.conn_id == DEFAULT_CONN_ID).first()
        conn.extra = json.dumps({"token": TOKEN})
        session.commit()
        self.hook = DatabricksHook()
class TestRunState:
    def test_is_terminal_true(self):
        terminal_states = ["TERMINATED", "SKIPPED", "INTERNAL_ERROR"]
        for state in terminal_states:
            run_state = RunState(state, "", "")
            assert run_state.is_terminal
    def test_is_terminal_false(self):
        non_terminal_states = ["PENDING", "RUNNING", "TERMINATING"]
        for state in non_terminal_states:
            run_state = RunState(state, "", "")
            assert not run_state.is_terminal
    def test_is_terminal_with_nonexistent_life_cycle_state(self):
        run_state = RunState("blah", "", "")
        with pytest.raises(AirflowException):
            assert run_state.is_terminal
    def test_is_successful(self):