You can do this with a few steps:
In multivariate platform or principle components platform, display the correlation matrix.
Then right-click the matrix and select "Make into Data Table".
 

 
Resulting in this new table
 

 
You will have to do some manipulation of that table of correlations:
Use Tables > Stack to create a tall version of the table that 3 columns:  A column with the variable names to use for categories on the X axis, another column with categories for the Y axis, and a column with correlation of the variables in the previous two columns. 
 

Now you can use the Graph Builder to create the heat map of these correlations.  I did have to reverse the order of the variables on the X axis in the axis settings to get what you wanted.
 

 
Here's a script that recreates all those steps on the sample data table ("Body Measurements.JMP") that I used to illustrate all of this.
 
dt = Open( "$SAMPLE_DATA/Body Measurements.jmp" );
pca = dt << Principal Components(
	Y(
		:Mass,
		:Fore,
		:Bicep,
		:Chest,
		:Neck,
		:Shoulder,
		:Waist,
		:Height,
		:Calf,
		:Thigh,
		:Head
	),
	Correlations( 1 ),
);
dtcorr = Report( pca )["Correlations"][Matrix Box( 1 )] <<
Make Into Data Table;
dtcorrstacked = dtcorr << Stack(
	columns(
		:Mass,
		:Fore,
		:Bicep,
		:Chest,
		:Neck,
		:Shoulder,
		:Waist,
		:Height,
		:Calf,
		:Thigh,
		:Head
	),
	Source Label Column( "VariableYAxis" ),
	Stacked Data Column( "Correlation" )
);
dtcorrstacked << Set Name( "Stacked Correlations" );
Close( dtcorr, NoSave );
Column( dtcorrstacked, "Row" ) << Set Name( "VariableXAxis" );
dtcorrstacked << Graph Builder(
	Size( 531, 531 ),
	Show Control Panel( 0 ),
	Graph Spacing( 4 ),
	Variables(
		X( :VariableXAxis ),
		Y( :VariableYAxis ),
		Color( :Correlation )
	),
	Elements( Heatmap( X, Y, Legend( 6 ) ) ),
	SendToReport(
		Dispatch( {}, "VariableXAxis", ScaleBox, {Reversed Scale} )
	)
);