**Apache Airflow version**:

1.10.12

**What happened**:

I created dag with dagrun_timeout=2 minutes. 

After 2 minutes dagrun is marked as failed and the next one is started, but task keeps going.

**What you expected to happen**:

Task is killed with dag run as it is done when you mark dagrun failed manually.

**How to reproduce it**:
```
dag = DAG(dag_id='platform.airflow-test',
          description='',
          schedule_interval="0 0 * * *",
          start_date=datetime(2020, 7, 1),
          max_active_runs=1,
          catchup=True,
          dagrun_timeout=timedelta(minutes=2))

run_this = BashOperator(
    task_id='run_after_loop',
    bash_command=' for((i=1;i<=600;i+=1)); do echo "Welcome $i times"; sleep 1;  done',
    dag=dag,
)
```
airflow/jobs/scheduler_job.py
self,
        file_path: str,
        callback_requests: List[CallbackRequest],
        pickle_dags: bool = False,
        session: Session = None,
    ) -> Tuple[int, int]:
        """
        Process a Python file containing Airflow DAGs.
        This includes:
        1. Execute the file and look for DAG objects in the namespace.
        2. Execute any Callbacks if passed to this method.
        3. Serialize the DAGs and save it to DB (or update existing record in the DB).
        4. Pickle the DAG and save it to the DB (if necessary).
        5. Record any errors importing the file into ORM
        :param file_path: the path to the Python file that should be executed
        :type file_path: str
        :param callback_requests: failure callback to execute
        :type callback_requests: List[airflow.utils.dag_processing.CallbackRequest]
        :param pickle_dags: whether serialize the DAGs found in the file and
            save them to the db
        :type pickle_dags: bool
        :param session: Sqlalchemy ORM Session
        :type session: Session
        :return: number of dags found, count of import errors
        :rtype: Tuple[int, int]
        """