Different outputs for normal distribution [closed]

You are using different tests, so there is no reason to expect them to produce the same results. Generally it is a bad idea to use a formal statistical test for normality since such tests can be heavily influenced by outliers and sample size. Even a very small departure from normality can be "significant" if the sample size is large enough. Furthermore, many models are extremely robust to departures from normality. A better way to assess normality is to simply inspect a histogram and a QQ plot.

I would highly recommend this Q&A on Cross Validated:
Is normality testing 'essentially useless'?

In short, each of those methods takes a slightly different approach to testing for the normality. Personally, I have tended to use the Shapiro-Wilk test, but each of those options is valid for some subset of data.

The primary question is the nature of the data.

This link gives a good overview of the differences between these: https://www.graphpad.com/guides/prism/latest/statistics/stat_choosing_a_normality_test.htm

Since your data looks like it is not uniquely valued, the Pearson test is probably where you would steer.