Wikipedia appears to have your answers. Here's an excerpt from the example statement of results:
In reporting the results of a Mann–Whitney test, it is important to state:
- A measure of the central tendencies of the two groups (means or medians; since the Mann–Whitney is an ordinal test, medians are usually recommended)
- The value of U
- The sample sizes
- The significance level.
In practice some of this information may already have been supplied and common sense should be used in deciding whether to repeat it. A typical report might run,
"Median latencies in groups E and C were 153 and 247 ms; the distributions in the two groups differed significantly (Mann–Whitney U = 10.5, n1 = n2 = 8, P < 0.05 two-tailed)."
The Wilcoxon signed-rank test is appropriate for paired samples, whereas the Mann–Whitney test assumes independent samples. However, according to Field (2000), the Wilcoxon $W$ in your SPSS output is "a different
version of this statistic, which can be converted into a Z score and can, therefore, be compared against critical values of the normal distribution." That explains your $z$ score too then!
FYI, Wikipedia adds that, for large samples, $U$ is approximately normally distributed. Given all these values, one can also calculate the effect size $η^2$, which in the case of Wikipedia's example is 0.319 (a calculator is implemented in section 11 here). However, this transformation of the test statistic depends on the approximate normality of $U$, so it might be inaccurate with ns = 8 (Fritz et al., 2012).
P.S. The Kruskal–Wallis test's results should not be interpreted as revealing differences between means except under special circumstances. See @Glen_b's answer to another question, "Difference Between ANOVA and Kruskal-Wallis test" for details.
References
Field, A. (2000). 3.1. Mann-Whitney test. Research Methods 1: SPSS for Windows part 3: Nonparametric tests. Retrieved from http://www.statisticshell.com/docs/nonparametric.pdf.
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. PDF available via ResearchGate.
Best Answer
The location-difference measure that the Mann-Whitney 'sees' is neither difference in means nor difference in medians -- it's the median of cross-group pairwise differences (the between samples quantity is the relevant estimate of the corresponding measure between populations).
See the end of this section of the wikipedia article on the Mann-Whitney (just above the section headed "Calculations").
The most typical additional assumptions required to make either the difference of means or medians reasonable (identity of distribution shapes is sufficient and is a commonly added assumption) immediately makes the other equally reasonable (at least assuming means are finite). So either: neither will be correct, or both should be good.