To solve the optimization problem described, we need to capture the objective function and all constraints in a mathematical model that can be translated into Gurobi code. The goal is to maximize the objective function `7*milkshakes + 1*sashimi` subject to several constraints related to calcium intake from milkshakes and sashimi.

### Objective Function:
- Maximize: `7*milkshakes + 1*sashimi`

### Constraints:
1. **Calcium Content**:
   - Milkshakes contain 2 milligrams of calcium each.
   - Sashimi contains 12 milligrams of calcium each.
   
2. **Total Calcium Requirement**:
   - At least 31 milligrams of calcium must come from milkshakes and sashimi.

3. **Calcium Limit**:
   - No more than 52 milligrams of calcium should come from milkshakes and sashimi.

4. **Linear Constraint**:
   - `-6*milkshakes + 7*sashimi >= 0`

5. **Integer Constraints**:
   - The number of milkshakes must be a non-negative integer.
   - The number of sashimi must be a non-negative integer.

### Gurobi Model:

```python
from gurobipy import *

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

# Define variables
milkshakes = m.addVar(vtype=GRB.INTEGER, name="milkshakes", lb=0)
sashimi = m.addVar(vtype=GRB.INTEGER, name="sashimi", lb=0)

# Objective function: Maximize 7*milkshakes + 1*sashimi
m.setObjective(7*milkshakes + 1*sashimi, GRB.MAXIMIZE)

# Constraint 1: Total calcium from milkshakes and sashimi is at least 31 milligrams
m.addConstr(2*milkshakes + 12*sashimi >= 31, name="calcium_min")

# Constraint 2: Total calcium from milkshakes and sashimi does not exceed 52 milligrams
m.addConstr(2*milkshakes + 12*sashimi <= 52, name="calcium_max")

# Constraint 3: Linear constraint -6*milkshakes + 7*sashimi >= 0
m.addConstr(-6*milkshakes + 7*sashimi >= 0, name="linear_constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milkshakes: {milkshakes.x}")
    print(f"Sashimi: {sashimi.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```
```python
```