## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as:
- $x_1$ : acres of mangoes
- $x_2$ : acres of pineapples

The objective is to maximize profit. Given that the profit per acre of mangoes is $400 and the profit per acre of pineapples is $450, the objective function can be written as:
\[ \text{Maximize:} \quad 400x_1 + 450x_2 \]

The constraints based on the given information are:
1. Total land: $x_1 + x_2 \leq 200$ (200 acres of land available)
2. Nutrients cost: $80x_1 + 100x_2 \leq 18000$ ($18000 available for nutrients)
3. Picking time: $2x_1 + 1.5x_2 \leq 350$ (350 hours available for picking)
4. Non-negativity: $x_1 \geq 0, x_2 \geq 0$ (acres cannot be negative)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of mangoes'), ('x2', 'acres of pineapples')],
    'objective_function': '400*x1 + 450*x2',
    'constraints': [
        'x1 + x2 <= 200',
        '80*x1 + 100*x2 <= 18000',
        '2*x1 + 1.5*x2 <= 350',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(lb=0, name="mangoes")
    x2 = model.addVar(lb=0, name="pineapples")

    # Objective function: Maximize 400*x1 + 450*x2
    model.setObjective(400*x1 + 450*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 200, name="land_constraint")
    model.addConstr(80*x1 + 100*x2 <= 18000, name="nutrients_constraint")
    model.addConstr(2*x1 + 1.5*x2 <= 350, name="picking_time_constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Acres of mangoes: {x1.varValue}")
        print(f"Acres of pineapples: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```