Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: kilograms of snow removed from the northern city.
* `y`: kilograms of snow removed from the southern city.

**Objective Function:**

Maximize profit: `2x + 1.5y`

**Constraints:**

* Snow plow time: `2x + y <= 500`
* Truck time: `x + 3y <= 500`
* Shovel time: `5x + 2y <= 500`
* Non-negativity: `x >= 0`, `y >= 0`


```python
import gurobipy as gp

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

# Create variables
x = m.addVar(lb=0, name="northern_city_snow")  # Snow removed from northern city (kg)
y = m.addVar(lb=0, name="southern_city_snow")  # Snow removed from southern city (kg)

# Set objective function
m.setObjective(2*x + 1.5*y, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x + y <= 500, "snow_plow_time")
m.addConstr(x + 3*y <= 500, "truck_time")
m.addConstr(5*x + 2*y <= 500, "shovel_time")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal snow removal:")
    print(f"  Northern City: {x.x:.2f} kg")
    print(f"  Southern City: {y.x:.2f} kg")
    print(f"  Total Profit: ${m.objVal:.2f}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
