Your data are paired, so the Mann-Whitney U test (which isn't) is immediately out.
We can easily argue for normality (the counts are large, if "$k$" means thousands, so near-normality would be reasonable), but the actual problem is that since for count data we expect the variance to be related to the mean, unless we assume that the mean isn't varying across days, we don't have constant variance, which might suggest some caution with the t-test; it will still have decent power, but its type I error rate may be somewhat affected.
You can argue for the Wilcoxon signed rank test, but there are several issues there that need to be thought about.
Note that there's also the sign test.
One could easily argue for a test that's appropriate for count data. So, for example, you could do a form of proportion test that takes account of the apparent one-tailedness of the hypothesis. (If you do a two-tailed test, you could also do it as a chi-square.)
Finally, one could also fit a Poisson, quasi-Poisson or Negative Binomial GLM.