```json
{
  "sym_variables": [
    ("x1", "policeman costumes"),
    ("x2", "fireman costumes")
  ],
  "objective_function": "8*x1 + 10*x2",
  "constraints": [
    "10*x1 + 15*x2 <= 3000",
    "x1 + x2 <= 280",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
policeman_costumes = m.addVar(vtype=gp.GRB.INTEGER, name="policeman_costumes")
fireman_costumes = m.addVar(vtype=gp.GRB.INTEGER, name="fireman_costumes")

# Set objective function
m.setObjective(8 * policeman_costumes + 10 * fireman_costumes, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(10 * policeman_costumes + 15 * fireman_costumes <= 3000, "budget_constraint")
m.addConstr(policeman_costumes + fireman_costumes <= 280, "demand_constraint")
m.addConstr(policeman_costumes >= 0, "policeman_nonnegative")
m.addConstr(fireman_costumes >= 0, "fireman_nonnegative")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal profit: {m.objVal}")
    print(f"Number of policeman costumes: {policeman_costumes.x}")
    print(f"Number of fireman costumes: {fireman_costumes.x}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
