On histograms it is very hard to see small deviations from normality. It is much easier to use a Q-Q plot (with qqnorm() and qqline()), which makes it more obvious where the data is non-normal.
As for statistical tests, their power directly depends on the sample size, so with a huge sample like yours any slight deviation from normality will make the p-value ridiculously small.
Remember: the p-value is not a measure of how important an effect is, just of whether random noise could cause it; so with a big enough sample size any irrelevantly small effect will bring your p-value to basically 0.
Which brings me to the real point: you almost never need a normality test. This rant explains it better than than I can, and gives a paper to cite.
If the conditions of the CLT are met, your sample data will be approximately distributed. But not exactly (since the population has no reason to be normal), so a big enough sample size will give a small p-value. If you had a very small sample size and something very non-normal, a normality test would fail to reject the null (because too small sample size), and mislead you to conclude your data is normal. Normality tests are almost never relevant, the only difficulty is to convince the reviewers of that fact.
Other relevant readings:
https://towardsdatascience.com/stop-testing-for-normality-dba96bb73f90