R^2 is a measure of how well the line fits the data (or how little error there is).

p is a measure of how likely it is that random data would produce what you see.

The reason your R^2 is high is because (as you can see) the line does really seem to fit well.

The reason your p isn't very low is because you only have 9 data points. The odds that random chance will look correlated is very high with such a small sample size. If you had a lot more data and the regression line still fit that well, you would see p be much smaller.

p is a measure of how likely it is that random data would produce what you see.

The reason your R^2 is high is because (as you can see) the line does really seem to fit well.

The reason your p isn't very low is because you only have 9 data points. The odds that random chance will look correlated is very high with such a small sample size. If you had a lot more data and the regression line still fit that well, you would see p be much smaller.