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

```python
import gurobipy as gp

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

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


# Set objective function
m.setObjective(300 * houses + 250 * apartments, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(2 * houses + apartments <= 600, "sweeping_constraint")
m.addConstr(2 * houses + 1.5 * apartments <= 700, "mopping_constraint")
m.addConstr(houses >= 0, "houses_nonnegative")
m.addConstr(apartments >= 0, "apartments_nonnegative")


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of houses to clean: {houses.x}")
    print(f"Number of apartments to clean: {apartments.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}")

```
