## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

* Let `x1` be the number of kilograms of snow removed in the northern city.
* Let `x2` be the number of kilograms of snow removed in the southern city.

The objective function is to maximize the profit, which is given by:

* `2 * x1 + 1.5 * x2`

The constraints are:

* The snow plow can be used for at most 500 minutes per day: `2 * x1 + 1 * x2 <= 500`
* The truck can be used for at most 500 minutes per day: `1 * x1 + 3 * x2 <= 500`
* The shovel can be used for at most 500 minutes per day: `5 * x1 + 2 * x2 <= 500`
* The number of kilograms of snow removed cannot be negative: `x1 >= 0`, `x2 >= 0`

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'kilograms of snow removed in the northern city'), ('x2', 'kilograms of snow removed in the southern city')],
    'objective_function': '2 * x1 + 1.5 * x2',
    'constraints': [
        '2 * x1 + 1 * x2 <= 500',
        '1 * x1 + 3 * x2 <= 500',
        '5 * x1 + 2 * x2 <= 500',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Snow Removal")

# Define the variables
x1 = model.addVar(name="x1", lb=0, ub=gp.GRB.INFINITY)  # kilograms of snow removed in the northern city
x2 = model.addVar(name="x2", lb=0, ub=gp.GRB.INFINITY)  # kilograms of snow removed in the southern city

# Define the objective function
model.setObjective(2 * x1 + 1.5 * x2, gp.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(2 * x1 + 1 * x2 <= 500, name="snow_plow_constraint")
model.addConstr(1 * x1 + 3 * x2 <= 500, name="truck_constraint")
model.addConstr(5 * x1 + 2 * x2 <= 500, name="shovel_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Kilograms of snow removed in the northern city: {x1.varValue}")
    print(f"Kilograms of snow removed in the southern city: {x2.varValue}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("No optimal solution found.")
```