To solve Bob's berry farm optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints.

Let's define:
- \(x_1\) as the number of acres for planting cranberries,
- \(x_2\) as the number of acres for planting bilberries.

The objective is to maximize profit. The profit per acre from cranberries is $66, and from bilberries is $73. Therefore, the objective function can be written as:
\[ \text{Maximize:} \quad 66x_1 + 73x_2 \]

Now, let's formulate the constraints based on the given information:
1. **Labor Constraint**: Each acre of cranberries requires 5 days of labor, and each acre of bilberries requires 4 days of labor. Bob has 600 days worth of labor available.
\[ 5x_1 + 4x_2 \leq 600 \]
2. **Watering Cost Constraint**: For each acre of cranberries, $25 in watering costs is required, and for each acre of bilberries, $30 in watering costs is required. Bob has $9000 to spend on watering.
\[ 25x_1 + 30x_2 \leq 9000 \]
3. **Non-negativity Constraints**: The number of acres cannot be negative.
\[ x_1 \geq 0, \quad x_2 \geq 0 \]
4. **Total Land Constraint**: Bob has a total of 250 acres available for planting.
\[ x_1 + x_2 \leq 250 \]

In symbolic representation with natural language objects:
```json
{
    'sym_variables': [('x1', 'acres of cranberries'), ('x2', 'acres of bilberries')],
    'objective_function': '66*x1 + 73*x2',
    'constraints': [
        '5*x1 + 4*x2 <= 600', 
        '25*x1 + 30*x2 <= 9000', 
        'x1 >= 0', 
        'x2 >= 0', 
        'x1 + x2 <= 250'
    ]
}
```

Now, let's write the Gurobi code to solve this linear programming problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="acres_of_cranberries")
x2 = m.addVar(lb=0, name="acres_of_bilberries")

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

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of cranberries: {x1.x}")
    print(f"Acres of bilberries: {x2.x}")
    print(f"Maximum profit: ${66*x1.x + 73*x2.x:.2f}")
else:
    print("No optimal solution found")
```