I agree that the plots suggest no obvious relationship between the two variables. But I want to mention some things.
Firstly, the correlation coefficient r for data like this could still be quite high and significant so I would say it is always a good idea to visualize the data as you did. Consider the following
# generate data
a <- c(rep(1, 100), 1:100) + rnorm(200, 0, 10)
b <- c(1:100, rep(1, 100)) + rnorm(200, 0, 1)
p-value < 2.2e-16
cor= - .56481
But it would be clearly inappropriate to assume a linear trend here.
Secondly, I don't think this is a big problem in your case judging by your plot but you still maybe want to read a little bit about overplotting. I suggest this because your can't really see much in the "tall cloud" in the range of latency_normalized 0 to 0.5.
Thirdly, the count-standardized variable is clearly right-skewed and zero-inflated. Maybe this is an issue but it depends on what you know about the data.
In the end I would say it depends on the meaning of the data how to interpret this plots. I am absolutely not sure about this interpretation but maybe there is some threshold going on here. The threshhold would be that there can't be any high count_standardized values once latency reached >=.5 (with a few exceptions, obviously). And maybe if you would apply transparency to the plot you would then see some relationship for the latency_standardized <.5 values with the count_standardized variable. But I can't tell whether this makes sense in your case to assume such a threshold.