### 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.
from airflow.datasets import Dataset
    from airflow.decorators import TaskDecoratorCollection
    from airflow.models.dagbag import DagBag
    from airflow.models.slamiss import SlaMiss
    from airflow.utils.task_group import TaskGroup
log = logging.getLogger(__name__)
DEFAULT_VIEW_PRESETS = ["grid", "graph", "duration", "gantt", "landing_times"]
ORIENTATION_PRESETS = ["LR", "TB", "RL", "BT"]
TAG_MAX_LEN = 100
DagStateChangeCallback = Callable[[Context], None]
ScheduleInterval = Union[None, str, timedelta, relativedelta]
ScheduleIntervalArg = Union[ArgNotSet, ScheduleInterval]
ScheduleArg = Union[ArgNotSet, ScheduleInterval, Timetable, Collection["Dataset"]]
SLAMissCallback = Callable[["DAG", str, str, List["SlaMiss"], List[TaskInstance]], None]
DEFAULT_SCHEDULE_INTERVAL = timedelta(days=1)
class InconsistentDataInterval(AirflowException):
    """Exception raised when a model populates data interval fields incorrectly.
    The data interval fields should either both be None (for runs scheduled
    prior to AIP-39), or both be datetime (for runs scheduled after AIP-39 is
    implemented). This is raised if exactly one of the fields is None.
    """
    _template = (
        "Inconsistent {cls}: {start[0]}={start[1]!r}, {end[0]}={end[1]!r}, "
        "they must be either both None or both datetime"
    )
    def __init__(self, instance: Any, start_field_name: str, end_field_name: str) -> None:
        self._class_name = type(instance).__name__
        self._start_field = (start_field_name, getattr(instance, start_field_name))
        self._end_field = (end_field_name, getattr(instance, end_field_name))
    def __str__(self) -> str: