## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the kilograms of Combo X to be made.
- $x_2$ represents the kilograms of Combo Y to be made.

## Step 2: Formulate the objective function
The profit from Combo X is $35 per kilogram, and from Combo Y is $55 per kilogram. Therefore, the objective function to maximize profit is:
\[ \text{Maximize:} \quad 35x_1 + 55x_2 \]

## 3: Define the constraints
1. **Almonds constraint**: Alpha Nut has 35 kilograms of almonds. Combo X is 70% almonds and Combo Y is 35% almonds. So, the constraint is:
\[ 0.70x_1 + 0.35x_2 \leq 35 \]
2. **Hazelnuts constraint**: Alpha Nut has 20 kilograms of hazelnuts. Combo X is 30% hazelnuts and Combo Y is 65% hazelnuts. So, the constraint is:
\[ 0.30x_1 + 0.65x_2 \leq 20 \]
3. **Non-negativity constraints**: The kilograms of each combo cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

## 4: Symbolic representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'Combo X (kg)'), ('x2', 'Combo Y (kg)')],
    'objective_function': '35*x1 + 55*x2',
    'constraints': [
        '0.70*x1 + 0.35*x2 <= 35',
        '0.30*x1 + 0.65*x2 <= 20',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

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

    # Define variables
    x1 = model.addVar(name="Combo_X", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="Combo_Y", lb=0, ub=gurobi.GRB.INFINITY)

    # Set objective function
    model.setObjective(35 * x1 + 55 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.70 * x1 + 0.35 * x2 <= 35, name="almonds_constraint")
    model.addConstr(0.30 * x1 + 0.65 * x2 <= 20, name="hazelnuts_constraint")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Combo X = {x1.varValue} kg, Combo Y = {x2.varValue} kg")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_nut_combo_problem()
```