### 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.
"""Test that when setting an individual mapped TI that the other TIs are not affected"""
    task_id = "t1"
    with dag_maker(session=session) as dag:
        @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()
        )
        mapped >> BaseOperator(task_id="downstream")
    dr1 = dag_maker.create_dagrun(
        run_type=DagRunType.SCHEDULED,
        state=DagRunState.FAILED,
    )
    expand_mapped_task(mapped, dr1.run_id, "make_arg_lists", length=2, session=session)
    
    dr2 = dag_maker.create_dagrun(
        run_type=DagRunType.SCHEDULED,
        state=DagRunState.FAILED,
        execution_date=DEFAULT_DATE + datetime.timedelta(days=1),
    )
    expand_mapped_task(mapped, dr2.run_id, "make_arg_lists", length=2, session=session)
    session.query(TI).filter_by(dag_id=dag.dag_id).update({"state": TaskInstanceState.FAILED})
    ti_query = (
        session.query(TI.task_id, TI.map_index, TI.run_id, TI.state)
        .filter(TI.dag_id == dag.dag_id, TI.task_id.in_([task_id, "downstream"]))