### 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.
session.add(DatasetModel(uri=dataset4.uri, is_orphaned=True))
            session.flush()
            dag1 = DAG(dag_id="datasets-1", start_date=DEFAULT_DATE, schedule=[dataset1])
            BashOperator(dag=dag1, task_id="task", bash_command="echo 1", outlets=[dataset3])
            DAG.bulk_write_to_db([dag1], session=session)
            
            non_orphaned_datasets = [
                dataset.uri
                for dataset in session.query(DatasetModel.uri)
                .filter(~DatasetModel.is_orphaned)
                .order_by(DatasetModel.uri)
            ]
            assert non_orphaned_datasets == ["ds1", "ds3"]
            orphaned_datasets = [
                dataset.uri
                for dataset in session.query(DatasetModel.uri)
                .filter(DatasetModel.is_orphaned)
                .order_by(DatasetModel.uri)
            ]
            assert orphaned_datasets == ["ds2", "ds4"]
            
            dag1 = DAG(dag_id="datasets-1", start_date=DEFAULT_DATE, schedule=[dataset1, dataset2])
            BashOperator(dag=dag1, task_id="task", bash_command="echo 1", outlets=[dataset3, dataset4])
            DAG.bulk_write_to_db([dag1], session=session)
            
            non_orphaned_dataset_count = session.query(DatasetModel).filter(~DatasetModel.is_orphaned).count()
            assert non_orphaned_dataset_count == 4
            orphaned_dataset_count = session.query(DatasetModel).filter(DatasetModel.is_orphaned).count()
            assert orphaned_dataset_count == 0
    def test_sync_to_db(self):