### 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.
stored_datasets = {}
        for dataset in all_datasets:
            stored_dataset = session.query(DatasetModel).filter(DatasetModel.uri == dataset.uri).first()
            if stored_dataset:
                
                
                
                stored_dataset.is_orphaned = expression.false()
                stored_datasets[stored_dataset.uri] = stored_dataset
            else:
                session.add(dataset)
                stored_datasets[dataset.uri] = dataset
        session.flush()  
        del all_datasets
        
        for dag_id, uri_list in dag_references.items():
            dag_refs_needed = {
                DagScheduleDatasetReference(dataset_id=stored_datasets[uri].id, dag_id=dag_id)
                for uri in uri_list
            }
            dag_refs_stored = set(
                existing_dags.get(dag_id)
                and existing_dags.get(dag_id).schedule_dataset_references  
                or []
            )
            dag_refs_to_add = {x for x in dag_refs_needed if x not in dag_refs_stored}
            session.bulk_save_objects(dag_refs_to_add)
            for obj in dag_refs_stored - dag_refs_needed:
                session.delete(obj)