## Step 1: Define the symbolic variables
Let's define the symbolic variables as follows:
- $x_1$ represents the acres of ylang ylang
- $x_2$ represents the acres of vanilla

## Step 2: Formulate the objective function
The profit per acre of ylang ylang is $150 and the profit per acre of vanilla is $100. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 150x_1 + 100x_2 \]

## 3: Define the constraints
Based on the problem description, the constraints are:
1. Total acres available: $x_1 + x_2 \leq 100$
2. Minimum acres of ylang ylang: $x_1 \geq 10$
3. Minimum acres of vanilla: $x_2 \geq 20$
4. Maximum ratio of ylang ylang to vanilla: $x_1 \leq 2x_2$

## 4: Symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'acres of ylang ylang'), ('x2', 'acres of vanilla')],
'objective_function': '150*x1 + 100*x2',
'constraints': [
    'x1 + x2 <= 100',
    'x1 >= 10',
    'x2 >= 20',
    'x1 <= 2*x2'
]
}
```

## 5: Convert to Gurobi code in Python
Now, let's implement this in Gurobi:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=10, name="x1")  # acres of ylang ylang
    x2 = model.addVar(lb=20, name="x2")  # acres of vanilla

    # Objective function
    model.setObjective(150 * x1 + 100 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 100, name="total_acres")
    model.addConstr(x1 <= 2 * x2, name="ratio_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal acres of ylang ylang: {x1.varValue}")
        print(f"Optimal acres of vanilla: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```