## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$ = acres of lavender
- $x_2$ = acres of tulips

The objective is to maximize profit, with the profit per acre of lavender being $250 and the profit per acre of tulips being $200. Therefore, the objective function can be represented as:

Maximize: $250x_1 + 200x_2$

The constraints given are:

1. The gardener has 50 acres available: $x_1 + x_2 \leq 50$
2. The gardener must grow at least 5 acres of lavender: $x_1 \geq 5$
3. The gardener must grow at least 8 acres of tulips: $x_2 \geq 8$
4. The gardener can grow at most twice the amount of lavender as tulips: $x_1 \leq 2x_2$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of lavender'), ('x2', 'acres of tulips')],
    'objective_function': '250*x1 + 200*x2',
    'constraints': [
        'x1 + x2 <= 50',
        'x1 >= 5',
        'x2 >= 8',
        'x1 <= 2*x2'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="lavender", lb=0)  # acres of lavender
x2 = model.addVar(name="tulips", lb=0)   # acres of tulips

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

# Constraints
model.addConstr(x1 + x2 <= 50, name="total_acres")
model.addConstr(x1 >= 5, name="min_lavender")
model.addConstr(x2 >= 8, name="min_tulips")
model.addConstr(x1 <= 2 * x2, name="lavender_vs_tulips")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of lavender: {x1.varValue}")
    print(f"Optimal acres of tulips: {x2.varValue}")
    print(f"Max Profit: ${250 * x1.varValue + 200 * x2.varValue}")
else:
    print("The model is infeasible.")
```