Use ~ as bitwise NOT:

df2.where(~F.col('Key').contains('sd')).show()

You can negate predicate using either not or ! so all what's left is to add another condition:

import org.apache.spark.sql.functions.not

df.where($"referrer".contains("www.mydomain.") &&
  not($"referrer".contains("google")))

or separate filter:

df
 .where($"referrer".contains("www.mydomain."))
 .where(!$"referrer".contains("google"))

In pyspark you can do it like this:

array = [1, 2, 3]
dataframe.filter(dataframe.column.isin(array) == False)

Or using the binary NOT operator:

dataframe.filter(~dataframe.column.isin(array))

Split text into tokens/words and use arrays_overlap function to check if wanted or unwanted token is present:

df = df.filter(
    (
      F.arrays_overlap(
          F.split(F.regexp_replace(F.lower("message"), r"[^a-zA-Z0-9\s]+", ""), "\s+"),
          F.array([F.lit(c) for c in wanted_words])
          )
    )
    & 
    (
      ~F.arrays_overlap(
          F.split(F.regexp_replace(F.lower("message"), r"[^a-zA-Z0-9\s]+", ""), "\s+"),
          F.array([F.lit(c) for c in unwanted_words])
          )
    )
)

Full example:

columns = ["id","message"]
data = [["ab123","Hello my name is Chris"],["cd345","The room should be 2301"],["ef567","Welcome! What is your name?"],["gh873","That way please"],["kj893","The current year is 2022"]]
df = spark.createDataFrame(data).toDF(*columns)

wanted_words = ['name','room']
unwanted_words = ['welcome','year']

df = df.filter(
    (
      F.arrays_overlap(
          F.split(F.regexp_replace(F.lower("message"), r"[^a-zA-Z0-9\s]+", ""), "\s+"),
          F.array([F.lit(c) for c in wanted_words])
          )
    )
    & 
    (
      ~F.arrays_overlap(
          F.split(F.regexp_replace(F.lower("message"), r"[^a-zA-Z0-9\s]+", ""), "\s+"),
          F.array([F.lit(c) for c in unwanted_words])
          )
    )
)

[Out]:
+-----+------------------------+
|id   |message                 |
+-----+------------------------+
|ab123|Hello my name is Chris  |
|cd345|The room should be 2301 |
+-----+------------------------+

You can also pre-compute the tokens at once for efficiency:

df = df.withColumn("tokens", F.split(F.regexp_replace(F.lower("message"), r"[^a-zA-Z0-9\s]+", ""), "\s+"))

and use in "arrays_overlap":

F.arrays_overlap(F.col("tokens"), F.array([F.lit(c) for c in wanted_words]))