### 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.
)
        task_instance_1 = TI(t_1, execution_date=DEFAULT_DATE, state=State.RUNNING)
        task_instance_2 = TI(t_2, execution_date=DEFAULT_DATE, state=State.RUNNING)
        session.merge(task_instance_1)
        session.merge(task_instance_2)
        return dag, subdag
    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state_for_subdag(self, dag_run_state):
        session = settings.Session()
        dag, subdag = self._make_test_subdag(session)
        session.flush()
        dag.clear(
            start_date=DEFAULT_DATE,
            end_date=DEFAULT_DATE + datetime.timedelta(days=1),
            dag_run_state=dag_run_state,
            include_subdags=True,
            include_parentdag=False,
            session=session,
        )
        dagrun = (
            session.query(
                DagRun,
            )
            .filter(DagRun.dag_id == subdag.dag_id)
            .one()
        )
        assert dagrun.state == dag_run_state
        session.rollback()
    @pytest.mark.parametrize("dag_run_state", [DagRunState.QUEUED, DagRunState.RUNNING])
    def test_clear_set_dagrun_state_for_parent_dag(self, dag_run_state):