### 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.
EmptyOperator(task_id=task_id, dag=dag2)
        DAG.bulk_write_to_db([dag1, dag2], session=session)
        session.commit()
        session.expunge_all()
        stored_datasets = {x.uri: x for x in session.query(DatasetModel).all()}
        d1_orm = stored_datasets[d1.uri]
        d2_orm = stored_datasets[d2.uri]
        assert [x.dag_id for x in d1_orm.consuming_dags] == []
        assert set(
            session.query(
                TaskOutletDatasetReference.task_id,
                TaskOutletDatasetReference.dag_id,
                TaskOutletDatasetReference.dataset_id,
            )
            .filter(TaskOutletDatasetReference.dag_id.in_((dag_id1, dag_id2)))
            .all()
        ) == {(task_id, dag_id1, d2_orm.id)}
    def test_bulk_write_to_db_unorphan_datasets(self):
        """
        Datasets can lose their last reference and be orphaned, but then if a reference to them reappears, we
        need to un-orphan those datasets
        """
        with create_session() as session:
            
            
            dataset1 = Dataset(uri="ds1")
            dataset2 = Dataset(uri="ds2")
            session.add(DatasetModel(uri=dataset2.uri, is_orphaned=True))
            dataset3 = Dataset(uri="ds3")
            dataset4 = Dataset(uri="ds4")