## Step 1: Define the symbolic representation of the problem
Let's denote the number of gourmet truffles as $x_1$ and the number of chocolate bars as $x_2$. The objective is to maximize profit, where each gourmet truffle gives a profit of $7 and each chocolate bar gives a profit of $3.

## Step 2: Formulate the objective function
The objective function to maximize profit is $7x_1 + 3x_2$.

## 3: Identify the constraints
1. **Cocoa availability constraint**: $700x_1 + 300x_2 \leq 30000$ (since there are 30,000 grams of cocoa available).
2. **Gourmet truffles to chocolate bars ratio constraint**: $x_2 \geq 2x_1$ (at least twice the amount of chocolate bars as gourmet truffles).
3. **Minimum gourmet truffles constraint**: $x_1 \geq 10$ (there needs to be at least 10 gourmet truffles made).
4. **Non-negativity constraints**: $x_1 \geq 0, x_2 \geq 0$ (the number of products cannot be negative).

## 4: Symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'gourmet truffles'), ('x2', 'chocolate bars')],
    'objective_function': '7*x1 + 3*x2',
    'constraints': [
        '700*x1 + 300*x2 <= 30000',
        'x2 >= 2*x1',
        'x1 >= 10',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 5: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="x1")  # Gourmet truffles
    x2 = model.addVar(lb=0, name="x2")  # Chocolate bars

    # Objective function: Maximize 7*x1 + 3*x2
    model.setObjective(7 * x1 + 3 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(700 * x1 + 300 * x2 <= 30000, name="cocoa_constraint")
    model.addConstr(x2 >= 2 * x1, name="ratio_constraint")
    model.addConstr(x1 >= 10, name="min_truffles_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Gourmet truffles: {x1.varValue}")
        print(f"Chocolate bars: {x2.varValue}")
        print(f"Maximum profit: {7 * x1.varValue + 3 * x2.varValue}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```