To solve the given optimization problem using Gurobi, we first need to understand and possibly simplify or clarify the constraints provided. The objective function to be maximized is \(4 \times \text{number of knishes} + 8 \times \text{number of kiwis}\).

Given constraints:
- Each knish contains 22 grams of carbohydrates and 14 milligrams of calcium.
- Each kiwi contains 28 grams of carbohydrates and 10 milligrams of calcium.
- The total carbohydrates from both should be at least 21 grams but no more than 49 grams (noted twice, implying the same constraint).
- The total calcium from both should be at least 40 milligrams but no more than 77 milligrams (also noted twice).
- A specific linear combination of knishes and kiwis: \(9 \times \text{number of knishes} - 3 \times \text{number of kiwis} \geq 0\).

The problem also specifies that the number of knishes must be an integer, while the number of kiwis can be a non-integer.

Let's denote:
- \(K\) as the number of knishes,
- \(W\) as the number of kiwis.

The objective function to maximize is: \(4K + 8W\).

Constraints:
1. Carbohydrates: \(22K + 28W \geq 21\) and \(22K + 28W \leq 49\).
2. Calcium: \(14K + 10W \geq 40\) and \(14K + 10W \leq 77\).
3. Linear combination: \(9K - 3W \geq 0\).

Since we're looking to maximize the objective function with these constraints, we can model this problem using Gurobi's Python interface as follows:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
K = m.addVar(vtype=GRB.INTEGER, name="knishes")  # Number of knishes (integer)
W = m.addVar(vtype=GRB.CONTINUOUS, name="kiwis")  # Number of kiwis (can be non-integer)

# Objective function: Maximize
m.setObjective(4*K + 8*W, GRB.MAXIMIZE)

# Constraints
m.addConstr(22*K + 28*W >= 21, "min_carbohydrates")
m.addConstr(22*K + 28*W <= 49, "max_carbohydrates")
m.addConstr(14*K + 10*W >= 40, "min_calcium")
m.addConstr(14*K + 10*W <= 77, "max_calcium")
m.addConstr(9*K - 3*W >= 0, "linear_combination")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: K={K.x}, W={W.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```