Here's the formulation and Gurobi code for the calculator production optimization problem:

**Decision Variables:**

* `x`: Number of scientific calculators produced.
* `y`: Number of graphing calculators produced.

**Objective Function:**

Maximize profit:  `6x + 8y`

**Constraints:**

* **Silicon:** `2x + 4y <= 100`
* **Plastic:** `4x + 6y <= 200`
* **Silver:** `x + 2y <= 50`
* **Non-negativity:** `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("calculator_production")

# Create variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="scientific")
y = m.addVar(vtype=GRB.CONTINUOUS, name="graphing")

# Set objective
m.setObjective(6 * x + 8 * y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2 * x + 4 * y <= 100, "silicon")
m.addConstr(4 * x + 6 * y <= 200, "plastic")
m.addConstr(x + 2 * y <= 50, "silver")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal profit: {m.objVal}")
    print(f"Number of scientific calculators: {x.x}")
    print(f"Number of graphing calculators: {y.x}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
