### 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_id = "test_clear_set_dagrun_state"
        self._clean_up(dag_id)
        task_id = "t1"
        dag = DAG(dag_id, start_date=DEFAULT_DATE, max_active_runs=1)
        @dag.task
        def make_arg_lists():
            return [[1], [2], [{"a": "b"}]]
        def consumer(value):
            print(value)
        mapped = PythonOperator.partial(task_id=task_id, dag=dag, python_callable=consumer).expand(
            op_args=make_arg_lists()
        )
        session = settings.Session()
        dagrun_1 = dag.create_dagrun(
            run_type=DagRunType.BACKFILL_JOB,
            state=State.FAILED,
            start_date=DEFAULT_DATE,
            execution_date=DEFAULT_DATE,
            session=session,
        )
        expand_mapped_task(mapped, dagrun_1.run_id, "make_arg_lists", length=2, session=session)
        upstream_ti = dagrun_1.get_task_instance("make_arg_lists", session=session)
        ti = dagrun_1.get_task_instance(task_id, map_index=0, session=session)
        ti2 = dagrun_1.get_task_instance(task_id, map_index=1, session=session)
        upstream_ti.state = State.SUCCESS
        ti.state = State.SUCCESS
        ti2.state = State.SUCCESS
        session.flush()
        dag.clear(
            task_ids=[(task_id, 0), ("make_arg_lists")],