### 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)

GET_RUN_RESPONSE = {
    "job_id": JOB_ID,
    "run_page_url": RUN_PAGE_URL,
    "state": {"life_cycle_state": LIFE_CYCLE_STATE, "state_message": STATE_MESSAGE},
}
GET_RUN_OUTPUT_RESPONSE = {"metadata": {}, "error": ERROR_MESSAGE, "notebook_output": {}}
NOTEBOOK_PARAMS = {"dry-run": "true", "oldest-time-to-consider": "1457570074236"}
JAR_PARAMS = ["param1", "param2"]
RESULT_STATE = ""
LIBRARIES = [
    {"jar": "dbfs:/mnt/libraries/library.jar"},
    {"maven": {"coordinates": "org.jsoup:jsoup:1.7.2", "exclusions": ["slf4j:slf4j"]}},
]
LIST_JOBS_RESPONSE = {
    "jobs": [
        {
            "job_id": JOB_ID,
            "settings": {
                "name": JOB_NAME,
            },
        },
    ],
    "has_more": False,
}
LIST_SPARK_VERSIONS_RESPONSE = {
    "versions": [
        {"key": "8.2.x-scala2.12", "name": "8.2 (includes Apache Spark 3.1.1, Scala 2.12)"},
    ]
}
def run_now_endpoint(host):