## Problem Description and Formulation

The problem is a classic example of a linear programming problem. Alpha Nut has a limited amount of almonds (35 kg) and hazelnuts (20 kg) and wants to maximize profit by producing two different combos of nuts: Combo X and Combo Y.

Let's define the decision variables:
- \(x\): the amount of Combo X produced in kilograms
- \(y\): the amount of Combo Y produced in kilograms

The objective is to maximize profit. Given that a kilogram of Combo X yields a profit of $35 and a kilogram of Combo Y yields a profit of $55, the objective function can be written as:
\[ \text{Maximize:} \quad 35x + 55y \]

There are constraints based on the availability of almonds and hazelnuts:
- Combo X is 70% almonds and 30% hazelnuts.
- Combo Y is 35% almonds and 65% hazelnuts.

So, the constraints can be formulated as:
- For almonds: \(0.70x + 0.35y \leq 35\)
- For hazelnuts: \(0.30x + 0.65y \leq 20\)

Additionally, \(x \geq 0\) and \(y \geq 0\) because the amount produced cannot be negative.

## Gurobi Code

```python
import gurobi

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

    # Define the decision variables
    x = model.addVar(name="Combo_X", lb=0, ub=None, obj=35)  # Amount of Combo X
    y = model.addVar(name="Combo_Y", lb=0, ub=None, obj=55)  # Amount of Combo Y

    # Define the constraints
    almonds_constraint = model.addConstr(0.70 * x + 0.35 * y <= 35, name="almonds_constraint")
    hazelnuts_constraint = model.addConstr(0.30 * x + 0.65 * y <= 20, name="hazelnuts_constraint")

    # Set the model objective to maximize
    model.setObjective(x.obj * x + y.obj * y, gurobi.GRB.MAXIMIZE)

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal amount of Combo X: {x.x}")
        print(f"Optimal amount of Combo Y: {y.x}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible.")

solve_alpha_nut_problem()
```

However, there seems to have been a slight mistake in directly translating your requirements into the code snippet above, particularly concerning how the objective function and variables' definition are handled in Gurobi. Let's refine that with accurate Gurobi syntax and practices:

```python
import gurobi

def solve_alpha_nut_problem():
    model = gurobi.Model()

    x = model.addVar(name="Combo_X", lb=0, obj=35)  
    y = model.addVar(name="Combo_Y", lb=0, obj=55)  

    almonds_constraint = model.addConstr(0.70 * x + 0.35 * y <= 35, name="almonds_constraint")
    hazelnuts_constraint = model.addConstr(0.30 * x + 0.65 * y <= 20, name="hazelnuts_constraint")

    model.setObjective(x.obj + y.obj, gurobi.GRB.MAXIMIZE)

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal amount of Combo X: {x.x}")
        print(f"Optimal amount of Combo Y: {y.x}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible.")

solve_alpha_nut_problem()
```