可以在DAG中添加一个新的Operator，在作业完成后将DAGRun状态标记为成功或失败。以下是一个示例代码：

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.models import DagRun
from datetime import datetime, timedelta

def mark_dag_run_status(**kwargs):
    dag_run_id = kwargs['dag_run'].run_id
    dag_run = DagRun.find(dag_id=kwargs['dag_run'].dag_id, run_id=dag_run_id)
    dag_run.state = 'success' if kwargs['ti'].state == 'success' else 'failed'
    dag_run.end_date = datetime.now()
    dag_run.external_trigger = False
    dag_run.verify_integrity()
    dag_run.log.info(f"Marking DagRun {dag_run_id} as {dag_run.state}")
    dag_run.update()

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2021, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=1)
}

dag = DAG('example_dag', default_args=default_args, schedule_interval=timedelta(days=1))

t1 = PythonOperator(
    task_id='task1',
    python_callable=lambda: print('Hello World!'),
    dag=dag
)

t2 = PythonOperator(
    task_id='mark_dagrun_status',
    python_callable=mark_dag_run_status,
    provide_context=True,
    dag=dag
)

t1 >> t2

在这个示例中，新的PythonOperator（t2）标记DAGRun状态为任务成功或失败，并设定end_date。使用provide_context=True向Python Operator提供上下文变量以获取DAG Run ID和Task Instance状态。最后，将t2插入DAG以启动标记过程。