## Step 1: Define the symbolic variables
Let's define the symbolic variables as follows:
- $x_1$ : amount invested in the chocolate company
- $x_2$ : amount invested in the coffee company
- $x_3$ : amount invested in the peanut butter company
- $x_4$ : amount invested in the maple syrup company

## Step 2: Formulate the objective function
The objective is to maximize the return on investment. The returns are 5% for the chocolate company, 10% for the coffee company, 7% for the peanut butter company, and 6% for the maple syrup company. Therefore, the objective function can be written as:
\[ \text{Maximize:} \quad 0.05x_1 + 0.10x_2 + 0.07x_3 + 0.06x_4 \]

## 3: Define the constraints
1. The total investment is $300,000:
\[ x_1 + x_2 + x_3 + x_4 \leq 300,000 \]
2. The amount invested in the chocolate company cannot exceed the amount invested in the maple syrup company:
\[ x_1 \leq x_4 \]
3. The amount invested in the coffee company cannot exceed the amount invested in the peanut butter company:
\[ x_2 \leq x_3 \]
4. At most 20% of her money can be invested in the maple syrup company:
\[ x_4 \leq 0.20 \times 300,000 \]
\[ x_4 \leq 60,000 \]
5. Non-negativity constraints:
\[ x_1, x_2, x_3, x_4 \geq 0 \]

## 4: Convert to Gurobi code
We will use the Gurobi library in Python to solve this linear programming problem.

```python
import gurobi as gp

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

# Define the variables
x1 = m.addVar(name="chocolate", lb=0)
x2 = m.addVar(name="coffee", lb=0)
x3 = m.addVar(name="peanut_butter", lb=0)
x4 = m.addVar(name="maple_syrup", lb=0)

# Objective function
m.setObjective(0.05*x1 + 0.10*x2 + 0.07*x3 + 0.06*x4, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 + x2 + x3 + x4 <= 300000, name="total_investment")
m.addConstr(x1 <= x4, name="chocolate_maple_syrup")
m.addConstr(x2 <= x3, name="coffee_peanut_butter")
m.addConstr(x4 <= 60000, name="maple_syrup_limit")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Chocolate: ${x1.varValue:.2f}")
    print(f"Coffee: ${x2.varValue:.2f}")
    print(f"Peanut Butter: ${x3.varValue:.2f}")
    print(f"Maple Syrup: ${x4.varValue:.2f}")
    print(f"Total Return: ${m.objVal:.2f}")
else:
    print("No optimal solution found.")
```

## 5: Symbolic Representation
Here is the symbolic representation of the problem:

```json
{
    'sym_variables': [
        ('x1', 'chocolate company'),
        ('x2', 'coffee company'),
        ('x3', 'peanut butter company'),
        ('x4', 'maple syrup company')
    ],
    'objective_function': '0.05x1 + 0.10x2 + 0.07x3 + 0.06x4',
    'constraints': [
        'x1 + x2 + x3 + x4 <= 300000',
        'x1 <= x4',
        'x2 <= x3',
        'x4 <= 60000',
        'x1, x2, x3, x4 >= 0'
    ]
}
```