To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints in algebraic terms.

Let's define:
- $x_1$ as the kilograms of Combo X produced.
- $x_2$ as the kilograms of Combo Y produced.

The objective is to maximize profit, where each kilogram of Combo X yields a profit of $35 and each kilogram of Combo Y yields a profit of $55. Thus, the objective function can be written as:
\[ \text{Maximize:} \quad 35x_1 + 55x_2 \]

The constraints are based on the availability of almonds and hazelnuts:
- Alpha Nut has 35 kilograms of almonds.
- Combo X is 70% almonds, so it uses $0.7x_1$ kilograms of almonds per kilogram of Combo X produced.
- Combo Y is 35% almonds, so it uses $0.35x_2$ kilograms of almonds per kilogram of Combo Y produced.
- The total almond usage should not exceed 35 kilograms: $0.7x_1 + 0.35x_2 \leq 35$.

Similarly, for hazelnuts:
- Alpha Nut has 20 kilograms of hazelnuts.
- Combo X is 30% hazelnuts, so it uses $0.3x_1$ kilograms of hazelnuts per kilogram of Combo X produced.
- Combo Y is 65% hazelnuts, so it uses $0.65x_2$ kilograms of hazelnuts per kilogram of Combo Y produced.
- The total hazelnut usage should not exceed 20 kilograms: $0.3x_1 + 0.65x_2 \leq 20$.

Additionally, the quantities of combos produced cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'kilograms of Combo X'), ('x2', 'kilograms of Combo Y')],
    'objective_function': '35*x1 + 55*x2',
    'constraints': ['0.7*x1 + 0.35*x2 <= 35', '0.3*x1 + 0.65*x2 <= 20', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

# Create a model
m = Model("Nut_Combo_Optimization")

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

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

# Add constraints
m.addConstr(0.7*x1 + 0.35*x2 <= 35, "Almond_Limit")
m.addConstr(0.3*x1 + 0.65*x2 <= 20, "Hazelnut_Limit")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Combo X: {x1.x} kg")
    print(f"Combo Y: {x2.x} kg")
    print(f"Maximum Profit: ${m.objVal}")
else:
    print("No optimal solution found.")
```