### 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.
dag_tags = set(dag.tags or {})
            orm_dag_tags = list(orm_dag.tags or [])
            for orm_tag in orm_dag_tags:
                if orm_tag.name not in dag_tags:
                    session.delete(orm_tag)
                    orm_dag.tags.remove(orm_tag)
            orm_tag_names = {t.name for t in orm_dag_tags}
            for dag_tag in dag_tags:
                if dag_tag not in orm_tag_names:
                    dag_tag_orm = DagTag(name=dag_tag, dag_id=dag.dag_id)
                    orm_dag.tags.append(dag_tag_orm)
                    session.add(dag_tag_orm)
            orm_dag_links = orm_dag.dag_owner_links or []
            for orm_dag_link in orm_dag_links:
                if orm_dag_link not in dag.owner_links:
                    session.delete(orm_dag_link)
            for owner_name, owner_link in dag.owner_links.items():
                dag_owner_orm = DagOwnerAttributes(dag_id=dag.dag_id, owner=owner_name, link=owner_link)
                session.add(dag_owner_orm)
        DagCode.bulk_sync_to_db(filelocs, session=session)
        from airflow.datasets import Dataset
        from airflow.models.dataset import (
            DagScheduleDatasetReference,
            DatasetModel,
            TaskOutletDatasetReference,
        )
        dag_references = collections.defaultdict(set)
        outlet_references = collections.defaultdict(set)
        
        outlet_datasets: dict[Dataset, None] = {}