### 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.
"""
        Ensure that datasets referenced in a dag are correctly loaded into the database.
        """
        dag_id1 = "test_dataset_dag1"
        dag_id2 = "test_dataset_dag2"
        task_id = "test_dataset_task"
        uri1 = "s3://dataset1"
        d1 = Dataset(uri1, extra={"not": "used"})
        d2 = Dataset("s3://dataset2")
        d3 = Dataset("s3://dataset3")
        dag1 = DAG(dag_id=dag_id1, start_date=DEFAULT_DATE, schedule=[d1])
        EmptyOperator(task_id=task_id, dag=dag1, outlets=[d2, d3])
        dag2 = DAG(dag_id=dag_id2, start_date=DEFAULT_DATE)
        EmptyOperator(task_id=task_id, dag=dag2, outlets=[Dataset(uri1, extra={"should": "be used"})])
        session = settings.Session()