### Description

Allow an Airflow DAG to define a Databricks job with the `api/2.1/jobs/create` (or `api/2.1/jobs/reset`) endpoint then run that same job with the `api/2.1/jobs/run-now` endpoint. This would give similar capabilities as the DatabricksSubmitRun operator, but the `api/2.1/jobs/create` endpoint supports additional parameters that the `api/2.1/jobs/runs/submit` doesn't (e.g. `job_clusters`, `email_notifications`, etc.).

### Use case/motivation

Create and run a Databricks job all in the Airflow DAG. Currently, DatabricksSubmitRun operator uses the `api/2.1/jobs/runs/submit` endpoint which doesn't support all features and creates runs that aren't tied to a job in the Databricks UI. Also, DatabricksRunNow operator requires you to define the job either directly in the Databricks UI or through a separate CI/CD pipeline causing the headache of having to change code in multiple places.

### 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)

This is an example DAG which uses the DatabricksSubmitRunOperator.
In this example, we create two tasks which execute sequentially.
The first task is to run a notebook at the workspace path "/test"
and the second task is to run a JAR uploaded to DBFS. Both,
tasks use new clusters.
Because we have set a downstream dependency on the notebook task,
the spark jar task will NOT run until the notebook task completes
successfully.
The definition of a successful run is if the run has a result_state of "SUCCESS".
For more information about the state of a run refer to
https://docs.databricks.com/api/latest/jobs.html#runstate
"""
from __future__ import annotations
import os
from datetime import datetime
from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksSubmitRunOperator
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "example_databricks_operator"
with DAG(
    dag_id=DAG_ID,
    schedule="@daily",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:
    
    
    new_cluster = {
        "spark_version": "9.1.x-scala2.12",