## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, as well as the constraints.

- Let \(x_1\) be the number of houses cleaned.
- Let \(x_2\) be the number of apartments cleaned.

The objective is to maximize profit. The profit per house cleaned is $300, and the profit per apartment cleaned is $250. Therefore, the objective function can be represented as:

\[ \text{Maximize:} \quad 300x_1 + 250x_2 \]

The constraints are based on the available hours for sweeping and mopping:

- Each house requires 2 hours of sweeping, and each apartment requires 1 hour of sweeping. The company has 600 hours available for sweeping:
\[ 2x_1 + x_2 \leq 600 \]

- Each house requires 2 hours of mopping, and each apartment requires 1.5 hours of mopping. The company has 700 hours available for mopping:
\[ 2x_1 + 1.5x_2 \leq 700 \]

Additionally, \(x_1 \geq 0\) and \(x_2 \geq 0\), since the number of houses and apartments cleaned cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'houses'), ('x2', 'apartments')],
    'objective_function': '300*x1 + 250*x2',
    'constraints': [
        '2*x1 + x2 <= 600',
        '2*x1 + 1.5*x2 <= 700',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="houses", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="apartments", lb=0, vtype=gp.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(300*x1 + 250*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(2*x1 + x2 <= 600, name="sweeping_hours")
model.addConstr(2*x1 + 1.5*x2 <= 700, name="mopping_hours")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found: houses = {x1.varValue}, apartments = {x2.varValue}")
    print(f"Maximum profit: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```