```json
{
  "sym_variables": [
    ("x1", "acres of pineapples"),
    ("x2", "acres of bananas")
  ],
  "objective_function": "200*x1 + 150*x2",
  "constraints": [
    "x1 + x2 <= 200",
    "x2 <= 4*x1",
    "x1 >= 40",
    "x2 >= 60"
  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
pineapples = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="pineapples")  # Acres of pineapples
bananas = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bananas")  # Acres of bananas


# Set objective function
m.setObjective(200 * pineapples + 150 * bananas, GRB.MAXIMIZE)

# Add constraints
m.addConstr(pineapples + bananas <= 200, "Land_Constraint")
m.addConstr(bananas <= 4 * pineapples, "Banana_Pineapple_Ratio")
m.addConstr(pineapples >= 40, "Min_Pineapples")
m.addConstr(bananas >= 60, "Min_Bananas")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Acres of Pineapples: {pineapples.x}")
    print(f"Acres of Bananas: {bananas.x}")
    print(f"Maximum Profit: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
