To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function along with the constraints.

- **Variables:**
  - \(x_1\): Acres of land used for growing pineapples.
  - \(x_2\): Acres of land used for growing bananas.

- **Objective Function:**
  The farmer aims to maximize profit. Given that the profit per acre of pineapples is $200 and the profit per acre of bananas is $150, the objective function can be represented as:
  \[ \text{Maximize} \quad 200x_1 + 150x_2 \]

- **Constraints:**
  1. The total land available is 200 acres:
     \[ x_1 + x_2 \leq 200 \]
  2. The farmer can grow at most 4 times the amount of bananas as pineapples:
     \[ x_2 \leq 4x_1 \]
  3. The farmer must grow at least 40 acres of pineapples:
     \[ x_1 \geq 40 \]
  4. The farmer must grow at least 60 acres of bananas:
     \[ x_2 \geq 60 \]
  5. Non-negativity constraints (since acres cannot be negative):
     \[ x_1 \geq 0, x_2 \geq 0 \]

However, since \(x_1\) and \(x_2\) are subject to lower bounds (\(x_1 \geq 40\) and \(x_2 \geq 60\)), the non-negativity constraints are implicitly satisfied.

Thus, the symbolic representation of the problem in JSON format is:
```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']
}
```

To solve this optimization problem using Gurobi in Python, we use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="pineapples", vtype=GRB.CONTINUOUS, lb=40)
x2 = m.addVar(name="bananas", vtype=GRB.CONTINUOUS, lb=60)

# Set the objective function
m.setObjective(200*x1 + 150*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 200, name="total_land")
m.addConstr(x2 <= 4*x1, name="banana_limit")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Pineapples: {x1.x}")
    print(f"Bananas: {x2.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```