```json
{
  "sym_variables": [
    ("x1", "marble countertops"),
    ("x2", "granite countertops")
  ],
  "objective_function": "500*x1 + 750*x2",
  "constraints": [
    "1*x1 + 1.5*x2 <= 300",
    "2*x1 + 3*x2 <= 500",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

# Create a new model
m = gp.Model("countertop_production")

# Create variables
marble = m.addVar(vtype=gp.GRB.CONTINUOUS, name="marble")
granite = m.addVar(vtype=gp.GRB.CONTINUOUS, name="granite")

# Set objective function
m.setObjective(500 * marble + 750 * granite, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(1 * marble + 1.5 * granite <= 300, "cutting_constraint")
m.addConstr(2 * marble + 3 * granite <= 500, "polishing_constraint")
m.addConstr(marble >= 0, "marble_nonnegativity")
m.addConstr(granite >= 0, "granite_nonnegativity")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"  Marble Countertops: {marble.x}")
    print(f"  Granite Countertops: {granite.x}")
    print(f"  Maximum Profit: ${m.objVal}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
