If you use the chi-squared this way, you can say that both dice are (possibily) not not fair.
If you use a chi-squared test, you test AGAINST the null-hypothesis that the numbers the dice roll are equally distributed, that each number will be rolled with the same probability. The alternative hypothesis (which normally is your idea you want to see proven in the data) would be “Certain numbers are rolled with higher probability than others”. If the chi-squared test gives you a result which has a probability below the significance level you agreed upon (in general 0.05 or 5%), you claim the alternative hypothesis seems to be “true”, if the result is larger than the significance-level, you stick with the null-hypothesis (“not significant… boohoo”).
However, in this case you should revert the hypotheses: Your alternative hypothesis (the one you claim) should be “All number roll with the same probability”, and your null-hypotheses (which is all that the alternative hypothesis is not) should be: “Some number roll with a higher probability”. That is not what the chi-squared test is made for as it tests the other way aorung. A hands-on-solution would be to take the hypothesis as reported above and adapt the level of significance: Increase it to make it harder to get a result which is higher than the significance level, e.g. to 30% oder 0.30. In this case the reported numbers of p=.126 and p=.172 (which I agree upon) are below the significance level, and we would assume with the alternative hypothesis: The distribution of numbers is not fair.
