How to ensure outer NULL join results output in spark streaming if the future events are delayed

Question

In a scenario of Spark stream-stream outer join:

val left = spark.readStream.format("delta").load("...")
.withWatermark("enqueuedTime", "1 hour")

val right = spark.readStream.format("delta").load("...")
.withWatermark("enqueuedTime", "1 hour")


val res = left.as("left").join(right.as("right"),
                               expr("left.key = right.key AND (left.enqueuedTime BETWEEN right.enqueuedTime - INTERVAL 1 hour AND right.enqueuedTime  + INTERVAL 1 hour)"), 
                               "left_outer")

res.writeStream(....)

And a data in left and right streams:

How to ensure a record:

2, left_value1, 2022-04-18T12:39:49.370+0000, NULL, NULL, NULL

is outputted after a given period of time even if new events aren't flowing thought the stream?

I'm only able to get it if new events arrive to both tables, like:

INSERT into left_df  VALUES ("004", "left_df_value", current_timestamp() + INTERVAL 5 hours);
INSERT into right_df VALUES ("004", "right_df_value", current_timestamp() + INTERVAL 5 hours);

Using which, Spark updates the watermarks and understands that now it's safe to output a nullable record. But how to still output it after some kind of timeout, without the new records arriving to both streams?