### 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.
width = 5
        depth = 5
        weight = 5
        pattern = re.compile("stage(\\d*).(\\d*)")
        
        
        
        with DAG("dag", start_date=DEFAULT_DATE, default_args={"owner": "owner1"}) as dag:
            pipeline = [
                [EmptyOperator(task_id=f"stage{i}.{j}", priority_weight=weight) for j in range(0, width)]
                for i in range(0, depth)
            ]
            for i, stage in enumerate(pipeline):
                if i == 0:
                    continue
                for current_task in stage:
                    for prev_task in pipeline[i - 1]:
                        current_task.set_upstream(prev_task)
            for task in dag.task_dict.values():
                match = pattern.match(task.task_id)
                task_depth = int(match.group(1))
                
                correct_weight = ((depth - (task_depth + 1)) * width + 1) * weight
                calculated_weight = task.priority_weight_total
                assert calculated_weight == correct_weight
    def test_dag_task_priority_weight_total_using_upstream(self):