### Description

Add Databricks Cluster API in Databricks Hook


### Use case/motivation

Databricks provider lacks [Cluster API](https://docs.databricks.com/dev-tools/api/latest/clusters.html) access to allow
to CRUD cluster as well as manage clusters (start/restart/delete/...)
Those API are very convenient to control clusters (get status, force shutdown...) and to optimize some specific flows (scale cluster in advance when a coming need is detected )

### Related issues

none

### 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", "QUEUED"]
        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):