### Apache Airflow version

main (development)

### What happened

Airflow has `job_heartbeat_sec` (default 5) that was updated to 50 seconds in our environment. This caused 2 instances of triggerer process running for HA to keep updating triggerer_id since below query takes current time minus 30 seconds to query against the latest_heartbeat. This causes alive_triggerer_ids to return empty list since job_heartbeat_sec is more than 50 seconds and the current trigger running this query assigns the triggers to itself. This keeps happening moving triggers from one instance to another.

https://github.com/apache/airflow/blob/62a534dbc7fa8ddb4c249ade85c558b64d1630dd/airflow/models/trigger.py#L216-L223

### What you think should happen instead

The heartbeat calculation should have a value based on job_heartbeat_sec rather than 30 seconds hardcoded so that queries to check triggerer processes alive are adjusted as per user settings.

### How to reproduce

1. Change job_heartbeat_sec to 50 in airflow.cfg
2. Run 2 instances of triggerer.
3. Make an operator that uses `FileTrigger` with the file being absent or any other long running task.
4. Check triggerer_id for the trigger which keeps changing. 

### Operating System

Ubuntu

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
        Handles outbound events from triggers - dispatching them into the Trigger
        model where they are then pushed into the relevant task instances.
        """
        while self.trigger_runner.events:
            
            trigger_id, event = self.trigger_runner.events.popleft()
            
            Trigger.submit_event(trigger_id=trigger_id, event=event)
            
            Stats.incr("triggers.succeeded")
    def handle_failed_triggers(self):
        """
        Handles "failed" triggers - ones that errored or exited before they
        sent an event. Task Instances that depend on them need failing.
        """
        while self.trigger_runner.failed_triggers:
            
            trigger_id, saved_exc = self.trigger_runner.failed_triggers.popleft()
            Trigger.submit_failure(trigger_id=trigger_id, exc=saved_exc)
            
            Stats.incr("triggers.failed")
    def emit_metrics(self):
        Stats.gauge(f"triggers.running.{self.job.hostname}", len(self.trigger_runner.triggers))
        Stats.gauge(
            "triggers.running", len(self.trigger_runner.triggers), tags={"hostname": self.job.hostname}
        )
class TriggerDetails(TypedDict):