## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

Let's denote:
- $x_1$ as the acres of blueberries
- $x_2$ as the acres of raspberries

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 200x_1 + 250x_2 \]

The constraints based on the given information are:
1. Land constraint: $x_1 + x_2 \leq 300$
2. Fertilizer cost constraint: $60x_1 + 40x_2 \leq 20000$
3. Picking time constraint: $3x_1 + 4x_2 \leq 400$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of blueberries'), ('x2', 'acres of raspberries')],
    'objective_function': '200*x1 + 250*x2',
    'constraints': [
        'x1 + x2 <= 300',
        '60*x1 + 40*x2 <= 20000',
        '3*x1 + 4*x2 <= 400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = m.addVar(name="blueberries", lb=0)  # Acres of blueberries
x2 = m.addVar(name="raspberries", lb=0)  # Acres of raspberries

# Objective function: Maximize profit
m.setObjective(200*x1 + 250*x2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 300, name="land_constraint")  # Land constraint
m.addConstr(60*x1 + 40*x2 <= 20000, name="fertilizer_constraint")  # Fertilizer cost constraint
m.addConstr(3*x1 + 4*x2 <= 400, name="picking_time_constraint")  # Picking time constraint

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of blueberries: {x1.varValue}")
    print(f"Optimal acres of raspberries: {x2.varValue}")
    print(f"Maximal profit: {m.objVal}")
else:
    print("The model is infeasible.")
```