### 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.
)
        ti1 = TI(task=test_task, run_id=dr1.run_id)
        ti1.state = None
        ti2 = TI(task=test_task, run_id=dr2.run_id)
        ti2.state = State.RUNNING
        ti3 = TI(task=test_task, run_id=dr3.run_id)
        ti3.state = State.QUEUED
        ti4 = TI(task=test_task, run_id=dr4.run_id)
        ti4.state = State.RUNNING
        session = settings.Session()
        session.merge(ti1)
        session.merge(ti2)
        session.merge(ti3)
        session.merge(ti4)
        session.commit()
        assert 0 == DAG.get_num_task_instances(test_dag_id, ["fakename"], session=session)
        assert 4 == DAG.get_num_task_instances(test_dag_id, [test_task_id], session=session)
        assert 4 == DAG.get_num_task_instances(test_dag_id, ["fakename", test_task_id], session=session)
        assert 1 == DAG.get_num_task_instances(test_dag_id, [test_task_id], states=[None], session=session)
        assert 2 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[State.RUNNING], session=session
        )
        assert 3 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[None, State.RUNNING], session=session
        )
        assert 4 == DAG.get_num_task_instances(
            test_dag_id, [test_task_id], states=[None, State.QUEUED, State.RUNNING], session=session
        )
        session.close()
    def test_get_task_instances_before(self):