### 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.models.dag import DagOwnerAttributes, dag as dag_decorator, get_dataset_triggered_next_run_info
from airflow.models.dataset import DatasetDagRunQueue, DatasetEvent, DatasetModel, TaskOutletDatasetReference
from airflow.models.param import DagParam, Param, ParamsDict
from airflow.models.serialized_dag import SerializedDagModel
from airflow.operators.bash import BashOperator
from airflow.operators.empty import EmptyOperator
from airflow.operators.python import PythonOperator
from airflow.operators.subdag import SubDagOperator
from airflow.security import permissions
from airflow.templates import NativeEnvironment, SandboxedEnvironment
from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable
from airflow.timetables.simple import DatasetTriggeredTimetable, NullTimetable, OnceTimetable
from airflow.utils import timezone
from airflow.utils.file import list_py_file_paths
from airflow.utils.session import create_session, provide_session
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.task_group import TaskGroup, TaskGroupContext
from airflow.utils.timezone import datetime as datetime_tz
from airflow.utils.types import DagRunType
from airflow.utils.weight_rule import WeightRule
from tests.models import DEFAULT_DATE
from tests.test_utils.asserts import assert_queries_count
from tests.test_utils.config import conf_vars
from tests.test_utils.db import clear_db_dags, clear_db_datasets, clear_db_runs, clear_db_serialized_dags
from tests.test_utils.mapping import expand_mapped_task
from tests.test_utils.timetables import cron_timetable, delta_timetable
TEST_DATE = datetime_tz(2015, 1, 2, 0, 0)
repo_root = Path(airflow.__file__).parent.parent
@pytest.fixture
def clear_dags():