How to check if all the columns of a row are null without hardcoding any column name in the query in spark?

You can flatten the (probably nested) columns in the dataframe and then check if each element of the flattened structure is null:

from pyspark.sql import functions as F
from pyspark.sql import types as T
from functools import reduce

#create some testdata with nested structures
data = ['{"active":null,"id":null,"uuid":null}', 
    '{"active":1,"id":2,"uuid":3}',
    '{"active":4,"id":5,"uuid":6, "x": {"y":7, "z":8, "a":{"b":9,"c":10}}}',
    '{"active":4,"id":5,"uuid":6, "x": {"y":7, "z":8, "a":{"b":9,"c":null}}}']

df = spark.read.json(spark.sparkContext.parallelize(data))

def flatten(field, prefix=""):
    if isinstance(field, T.StructType):
        for f in field:
            yield from flatten(f, prefix)
    elif isinstance(field, T.StructField) and isinstance(field.dataType, T.StructType):
        for f in field.dataType:
            yield from flatten(f, prefix + field.name + ".")
    else:
        yield prefix + field.name

cols_are_null = [F.col(c).isNull() for c in flatten(df.schema)]
all_cols_are_null = reduce(lambda l,r: l & r, cols_are_null)
df.withColumn("all_cols_are_null", all_cols_are_null).show()

#+------+----+----+-----------------+-----------------+
#|active|  id|uuid|                x|all_cols_are_null|
#+------+----+----+-----------------+-----------------+
#|  null|null|null|             null|             true|
#|     1|   2|   3|             null|            false|
#|     4|   5|   6|  {{9, 10}, 7, 8}|            false|
#|     4|   5|   6|{{9, null}, 7, 8}|            false|
#+------+----+----+-----------------+-----------------+