### 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.
)
    max_active_tasks = Column(Integer, nullable=False)
    max_active_runs = Column(Integer, nullable=True)
    has_task_concurrency_limits = Column(Boolean, nullable=False)
    has_import_errors = Column(Boolean(), default=False, server_default="0")
    
    next_dagrun = Column(UtcDateTime)
    
    next_dagrun_data_interval_start = Column(UtcDateTime)
    next_dagrun_data_interval_end = Column(UtcDateTime)
    
    next_dagrun_create_after = Column(UtcDateTime)
    __table_args__ = (
        Index("idx_root_dag_id", root_dag_id, unique=False),
        Index("idx_next_dagrun_create_after", next_dagrun_create_after, unique=False),
    )
    parent_dag = relationship(
        "DagModel", remote_side=[dag_id], primaryjoin=root_dag_id == dag_id, foreign_keys=[root_dag_id]
    )
    schedule_dataset_references = relationship(
        "DagScheduleDatasetReference",
        cascade="all, delete, delete-orphan",
    )
    schedule_datasets = association_proxy("schedule_dataset_references", "dataset")
    task_outlet_dataset_references = relationship(
        "TaskOutletDatasetReference",
        cascade="all, delete, delete-orphan",
    )
    NUM_DAGS_PER_DAGRUN_QUERY = conf.getint("scheduler", "max_dagruns_to_create_per_loop", fallback=10)
    def __init__(self, concurrency=None, **kwargs):