Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Acres of pineapples to grow
* `y`: Acres of bananas to grow

**Objective Function:**

Maximize profit: `200x + 150y`

**Constraints:**

* **Land availability:** `x + y <= 200`
* **Banana to pineapple ratio:** `y <= 4x`
* **Minimum pineapples:** `x >= 40`
* **Minimum bananas:** `y >= 60`
* **Non-negativity:** `x >= 0`, `y >= 0`


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

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

# Create decision variables
x = m.addVar(lb=0, name="Pineapples")  # Acres of pineapples
y = m.addVar(lb=0, name="Bananas")  # Acres of bananas

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

# Add constraints
m.addConstr(x + y <= 200, "Land_Availability")
m.addConstr(y <= 4*x, "Banana_Pineapple_Ratio")
m.addConstr(x >= 40, "Min_Pineapples")
m.addConstr(y >= 60, "Min_Bananas")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Pineapples: {x.x} acres")
    print(f"Bananas: {y.x} acres")
    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}")

```
