I assume you mean how to label the image and class inputs since the discriminator can reasonably output either "real" or "fake" labels for either of those inputs, and you generally want to be training with an imperfect discriminator.
In both your scenarios the correct ground truth for training the discriminator is "fake", although it may be better to think of it as "incorrect" in the case of mislabeled real inputs.
You may also reasonably decide not to train with mislabeled real images. They are not necessary, and although they might improve the discriminator training, that's not going to make a difference for the MNIST digits task.
You shouldn't train with deliberately mislabeled generator images, either. If the generator accidentally makes a "1" when you asked it to generate a "7", then you should label ground truth as "fake" for training the discriminator and "real" for training the generator, plus in both cases you should include the attempted "7" as input to the discriminator alongside the generated image