### Body

There are some use cases where users want to trigger new DAG run as soon as one finished. This is a request I've seen several times with some variations (for example like this [Stackoverflow question](https://stackoverflow.com/q/75623153/14624409)) but the basic request is the same.

The workaround users do to get such functionality is place `TriggerDagRunOperator` as last task of their DAG invoking the same DAG:

```
from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule=None,
) as dag:
    task = EmptyOperator(task_id="first")
    trigger = TriggerDagRunOperator(
        task_id="trigger",
        trigger_dag_id="example",
    )

    task >> trigger

```

As you can see this works nicely:
![Screenshot 2023-03-03 at 14 20 51](https://user-images.githubusercontent.com/45845474/222718862-dfde8a41-24b6-4991-b318-b7f9784514f6.png)

My suggestion is to add first class support for this use case, so the above example will be changed to:

```

from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule="@continues",
) as dag:
    task = EmptyOperator(task_id="first")
```

I guess it won't exactly be `"@continues"` but more likely new [ScheduleArg](https://github.com/apache/airflow/blob/8b8552f5c4111fe0732067d7af06aa5285498a79/airflow/models/dag.py#L127) type but I show it like that just for simplification of the idea.

### Committer

- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
dag = DAG("my-dag")  
    dag.timetable = timetable
    assert not dag._check_schedule_interval_matches_timetable()
@pytest.mark.parametrize("run_id, execution_date", [(None, datetime_tz(2020, 1, 1)), ("test-run-id", None)])
def test_set_task_instance_state(run_id, execution_date, session, dag_maker):
    """Test that set_task_instance_state updates the TaskInstance state and clear downstream failed"""
    start_date = datetime_tz(2020, 1, 1)
    with dag_maker("test_set_task_instance_state", start_date=start_date, session=session) as dag:
        task_1 = EmptyOperator(task_id="task_1")
        task_2 = EmptyOperator(task_id="task_2")
        task_3 = EmptyOperator(task_id="task_3")
        task_4 = EmptyOperator(task_id="task_4")
        task_5 = EmptyOperator(task_id="task_5")
        task_1 >> [task_2, task_3, task_4, task_5]
    dagrun = dag_maker.create_dagrun(
        run_id=run_id,
        execution_date=execution_date,
        state=State.FAILED,
        run_type=DagRunType.SCHEDULED,
    )
    def get_ti_from_db(task):
        return (
            session.query(TI)
            .filter(
                TI.dag_id == dag.dag_id,
                TI.task_id == task.task_id,
                TI.run_id == dagrun.run_id,
            )
            .one()
        )