Fitted values are those which are predicted from a linear regression equation, whereas residuals are how well this "guess" was at actually fitting the regression line. In an ideal world, we want the residuals and fitted values to have no pattern and to be as similar in magnitude away from the center line as possible.
Without knowing the data exactly in this example, it appears the residual for this specific point is very far away from $0$, which indicates that overall the regression guessed this value pretty poorly compared to the others. There could be a variety of reasons for this, including outlier values, poor fit to the actual function of data (e.g. fitting a linear relationship to a nonlinear one), unequal variance around the mean, and other influences.
AsHere are some examples.
Example 1
This is a linear fit that does a good job overall of fitting the data around the mean, though you can see that because the variance isn't completely evenly distributed around the regression line, it curves at times around the residual plot line (because of less data on the tails in the actual scatterplot of data).
Example 2
This is a fit with an exampleextreme outlier. Here you can see on the fitted/residuals plot that the outlier appears far to the right, but it can also look like yours where it is far away on the y-axis.
Example 3
Here is what it can look like with nonlinear fits, where it resembles a u-shape here since it both under/overpredicts the curve.
