### 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.
execution_date=pendulum.now("UTC"),
        )
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == []
        
        dag_maker.dag_model.max_active_runs = 2
        session.flush()
        query, _ = DagModel.dags_needing_dagruns(session)
        dag_models = query.all()
        assert dag_models == [dag_model]
    def test_max_active_runs_not_none(self):
        dag = DAG(dag_id="test_max_active_runs_not_none", start_date=timezone.datetime(2038, 1, 1))
        EmptyOperator(task_id="dummy", dag=dag, owner="airflow")
        session = settings.Session()
        orm_dag = DagModel(
            dag_id=dag.dag_id,
            has_task_concurrency_limits=False,
            next_dagrun=None,
            next_dagrun_create_after=None,
            is_active=True,
        )
        
        assert orm_dag.max_active_runs == 16
        session.add(orm_dag)
        session.flush()
        assert orm_dag.max_active_runs is not None
        session.rollback()
        session.close()
    def test_dags_needing_dagruns_only_unpaused(self):