## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of cranberries
- $x_2$ represents the acres of bilberries

## Step 2: Formulate the objective function
The profit per acre of cranberries is $66 and the profit per acre of bilberries is $73. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 66x_1 + 73x_2 \]

## 3: Define the constraints
1. **Land constraint**: Bob has a 250 acre berry farm.
\[ x_1 + x_2 \leq 250 \]
2. **Watering cost constraint**: Bob has $9000 to spend on watering.
\[ 25x_1 + 30x_2 \leq 9000 \]
3. **Labor constraint**: Bob has 600 days worth of labor available.
\[ 5x_1 + 4x_2 \leq 600 \]
4. **Non-negativity constraints**: Acres cannot be negative.
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

## 4: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'acres of cranberries'), ('x2', 'acres of bilberries')],
    'objective_function': '66*x1 + 73*x2',
    'constraints': [
        'x1 + x2 <= 250',
        '25*x1 + 30*x2 <= 9000',
        '5*x1 + 4*x2 <= 600',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 5: Gurobi code to solve the LP problem
```python
import gurobipy as gp

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

# Define the variables
x1 = m.addVar(name="cranberries", lb=0)
x2 = m.addVar(name="bilberries", lb=0)

# Define the objective function
m.setObjective(66*x1 + 73*x2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 250, name="land_constraint")
m.addConstr(25*x1 + 30*x2 <= 9000, name="watering_cost_constraint")
m.addConstr(5*x1 + 4*x2 <= 600, name="labor_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Acres of cranberries: {x1.varValue}")
    print(f"Acres of bilberries: {x2.varValue}")
    print(f"Max profit: {m.objVal}")
else:
    print("No optimal solution found.")
```