Histogram residual not lining up with normality test This histogram residual looks less normal than this one (I think), yet the latter is giving me a lower p-value for the Jarque-Bera normality test. Specifically, the latter gives a p-value of 0.14 whilst the former gives a p-value of 0.56! Why are the results not lining up?
 A: You have a lot of bins (10) for few data points (100). Even perfectly normal distributions can yield "non-normal-looking" histograms in such cases. (Compare this.)
Simulations are always good to get an idea of what we can reasonably expect under specific hypotheses - here, under the assumption of a normal distribution. For instance, let's simulate 100 normally distributed observations and plot them in a histogram with 10 bins, as in your case. Let's do this six times, just to get a feeling:

As you see, these histograms can look pretty non-normal indeed - all but one of them are even multimodal!
The important point is that these are all equally normal. After all, they were all generated with the same (normal) distribution. Thus, in particular, the shapes of your two histograms are both perfectly consistent with a normal distribution assumption.
And under the assumption of a normal distribution, the p value does not say anything about non-normality - under the null hypothesis, the p values are uniformly distributed between zero and one (see the dance of the p values). In particular, you cannot infer anything from one of your p values being larger than the other.
R code:
set.seed(1) # for reproducibility
ss <- 1
nn <- 100

sims <- matrix(rnorm(6*nn,0,ss),nrow=6)

par(mfrow=c(2,3),las=1)
for ( ii in 1:6 ) hist(sims[ii,],xlim=range(sims),breaks=10,xlab="",ylab="",main="")

