To solve this optimization problem, we will use linear programming. The goal is to maximize profit given the constraints on cereal units and filling time.

Let's denote:
- \(x\) as the number of individual cereal boxes made,
- \(y\) as the number of family size cereal boxes made.

The objective function (to be maximized) represents the total profit, which can be calculated as follows: 
\[ \text{Profit} = 4x + 8y \]

We have two main constraints:
1. The total amount of cereal used does not exceed 2000 units:
\[ 20x + 60y \leq 2000 \]
2. The total time spent filling boxes does not exceed 750 minutes:
\[ 10x + 15y \leq 750 \]

Additionally, \(x\) and \(y\) must be non-negative since they represent quantities of cereal boxes.

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

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

# Define variables
x = m.addVar(lb=0, vtype=GRB.INTEGER, name="individual_boxes")
y = m.addVar(lb=0, vtype=GRB.INTEGER, name="family_size_boxes")

# Set the objective function: Maximize profit
m.setObjective(4*x + 8*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(20*x + 60*y <= 2000, "cereal_limit")
m.addConstr(10*x + 15*y <= 750, "time_limit")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x.varName} = {x.x}, {y.varName} = {y.x}")
    print(f"Maximum profit: ${m.objVal}")
else:
    print("No optimal solution found")
```