Inefficient DNA heterodimerization script
Here is your original code with comments on which lines should be revised for clarity
import sys
from Bio import SeqIO
from Bio.Seq import Seq

def hetero_dimerization():
    script = sys.argv[0]  # script is unused; the line should be removed
    file1 = sys.argv[1]
    list = []  # list is a python built-in python; overwrite it at your own peril. I'd suggest renaming this variable
    list2 = []
    with open(file1, 'r') as file:
        for record in SeqIO.parse(file, 'fasta'):
            for i in range(len(record.seq)):
                kmer = str(record.seq[i:i + 6])
                if len(kmer) == 6:  # this check is redundant. kmer is guaranteed to be of length 6
                    list.append(kmer)
            #print(record.seq)  # if you submit code for review, avoid commented out source code. it is confusing
            #print(list)  # same here, avoid commented out code

            # this is a bug. You add every kmer to the list even if you discard the sequence
            # you also add redundant ones, which causes considerable slowdown
            for kmers in list:
                C_kmer = Seq(kmers).complement()
                list2.append(C_kmer[::-1])
            #print(list2)  # avoid commented out code

            # adding a counter for a simple if is not very clean
            # instead consider `if not any(...):`
            cnt=0
            if any(items in record.seq for items in list2):
                cnt +=1

            if cnt == 0:
                print('&gt;'+record.id)
                print(record.seq)
                
if __name__ == '__main__':
    hetero_dimerization()


Your code contains a bug (@danzel already pointed that out), as you append the list if inverse complements for every 6mer you encounter, irrespective of it being from a sequence you wish to discard and irrespective of it being already contained in the list. You've fixed the bug in your updated code and in the process also changed the algorithm to avoid a lot of unnecessary work.
You can slightly (~15%) improve this further by (a) switching the complement list to a set and searching the set for each 6mer in the sequence (instead of the other way around) and by stopping the search early (breaking) if you encounter a 6mer that is already in the list. I also took the liberty to python-ify your code a bit in the process.
def kmer_generator(record, k=6):
    sequence = record.seq
    for idx in range(len(sequence) - (k-1)):
        yield sequence[idx:(idx+6)]

def hetero_dimerization_optimized():
    taboo_list = set()
    resulting_sequences = list()
    with open(&quot;fasta-file&quot;, 'r') as file:
        for record in SeqIO.parse(file, 'fasta'):
            new_taboo_items = set()
            for kmer in kmer_generator(record):
                if str(kmer) in taboo_list:
                    break
                complement = str(kmer.reverse_complement())
                new_taboo_items.add(complement)
            else:
                # this is entered only if no break occurs
                # i.e. if no kmer was in the taboo_list :)
                taboo_list = taboo_list.union(new_taboo_items)
                resulting_sequences.append(record.seq)
    return resulting_sequences

From here, your most noticable speedups would come from (a) writing your own reverse_complement function that reduces constant overhead (~50% of the runtime is spend on this line), (b) switching to numpy and using chararray to get access to np.view and avoid further copies of your data; this is in addition to (a) and could save you an additional 20%+ runtime.
After this, you could look into multiprocessing to use additional cores to pre-filter sequences, but tbh. this will likely be over-engieered for a mere 9000 sequences.