To solve the optimization problem described, 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 of ylang ylang grown,
- $x_2$ as the number of acres of vanilla grown.

The objective function, which aims to maximize profit, can be represented as:
\[ 150x_1 + 100x_2 \]

Given the constraints:
1. The producer must grow at least 10 acres of ylang ylang: $x_1 \geq 10$
2. The producer must grow at least 20 acres of vanilla: $x_2 \geq 20$
3. The total land used cannot exceed 100 acres: $x_1 + x_2 \leq 100$
4. The amount of ylang ylang grown can be at most twice the amount of vanilla: $x_1 \leq 2x_2$

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

To solve this problem using Gurobi in Python, we can write the following code:

```python
from gurobipy import *

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

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

# Set the objective function to maximize profit
m.setObjective(150*x1 + 100*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 10, "Minimum_Ylang_Ylang")
m.addConstr(x2 >= 20, "Minimum_Vanilla")
m.addConstr(x1 + x2 <= 100, "Total_Land")
m.addConstr(x1 <= 2*x2, "Ylang_Ylang_to_Vanilla_Ratio")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: Grow {x1.x} acres of ylang ylang and {x2.x} acres of vanilla.")
else:
    print("No optimal solution found.")
```