```json
{
  "sym_variables": [
    ("x1", "servings of crab soup"),
    ("x2", "servings of lobster soup")
  ],
  "objective_function": "3*x1 + 5*x2",
  "constraints": [
    "7*x1 + 10*x2 <= 80",
    "8*x1 <= 65",
    "5*x2 <= 55",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
crab_soup = m.addVar(vtype=gp.GRB.CONTINUOUS, name="crab_soup")  # x1
lobster_soup = m.addVar(vtype=gp.GRB.CONTINUOUS, name="lobster_soup")  # x2

# Set objective function
m.setObjective(3 * crab_soup + 5 * lobster_soup, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(7 * crab_soup + 10 * lobster_soup <= 80, "water_constraint")
m.addConstr(8 * crab_soup <= 65, "crab_meat_constraint")
m.addConstr(5 * lobster_soup <= 55, "lobster_meat_constraint")
m.addConstr(crab_soup >= 0, "crab_soup_nonnegativity")  # Explicit non-negativity constraint
m.addConstr(lobster_soup >= 0, "lobster_soup_nonnegativity")  # Explicit non-negativity constraint


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal profit: ${m.objVal}")
    print(f"Servings of crab soup: {crab_soup.x}")
    print(f"Servings of lobster soup: {lobster_soup.x}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
