Try Case statement, not sure this CASE statement is supported by Spark SQL:-

select a.x, a.x1,
      CASE WHEN b.x1 IS NULL THEN a.x1
           ELSE b.x1
      END as bx1
from t1 a LEFT OUTER JOIN t2 b on a.x1=b.x1;

I am referring to the documentation here: Databricks Update Reference

In specific this syntax:

UPDATE table_name [table_alias]
SET  { { column_name | field_name }  = [ expr | DEFAULT } [, ...]
[WHERE clause]

And I'm referring to the function from here: Databricks Built-In Functions , it's on the last section titled Miscellaneous functions, from this section we're using the nullif command.

nullif(expr1, expr2) : Returns NULL if expr1 equals expr2, or expr1 otherwise.

This allows us to update the table in this way:

update db.table_name
set column_1 = nullif(column_1, '')

This syntax above updates the column_1 in the case that the value in column_1 matches the empty string ''. If it doesn't match the empty string then the row stays as it is, with whatever value is already there.

In this situation UPDATE with NULLIF is better than UPDATE with REPLACE because REPLACE is a search, delete and insert.

In general it is more inefficient to use REPLACE because it performs a search along your specified key whereas NULLIF is a predicate match that can implicitly filter under the hood.

nullif documentation