To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of acres for blueberries,
- $x_2$ as the number of acres for raspberries.

The objective function is to maximize profit. Given that the profit per acre of blueberries is $200 and the profit per acre of raspberries is $250, the objective function can be represented as:

Maximize: $200x_1 + 250x_2$

The constraints are:
1. The total cost for fertilizer does not exceed $20,000: $60x_1 + 40x_2 \leq 20000$
2. The total hours of picking do not exceed 400 hours: $3x_1 + 4x_2 \leq 400$
3. The total acres used do not exceed 300 acres: $x_1 + x_2 \leq 300$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$ since the number of acres cannot be negative.

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

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="blueberries", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="raspberries", lb=0)

# Set the objective function
m.setObjective(200*x1 + 250*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(60*x1 + 40*x2 <= 20000, "fertilizer_budget")
m.addConstr(3*x1 + 4*x2 <= 400, "picking_hours")
m.addConstr(x1 + x2 <= 300, "total_acres")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of blueberries: {x1.x}")
    print(f"Acres of raspberries: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```