The provided information describes a linear programming problem. We aim to minimize the objective function while satisfying all given constraints.  The Gurobi code below models this problem. Redundant constraints (e.g., "at least" and "greater than or equal to") are combined into a single constraint.

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

try:
    # Create a new model
    m = gp.Model("nutrition_optimization")

    # Create variables
    vitamin_d = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="vitamin_d")  # milligrams of vitamin D
    protein = m.addVar(lb=0, vtype=GRB.INTEGER, name="protein")  # grams of protein

    # Set objective function
    m.setObjective(8.27 * vitamin_d + 9.25 * protein, GRB.MINIMIZE)

    # Add constraints
    m.addConstr(2.3 * vitamin_d + 0.28 * protein >= 22, "muscle_growth_lower")
    m.addConstr(0.95 * vitamin_d + 1.94 * protein >= 15, "energy_stability_lower")
    m.addConstr(2.92 * vitamin_d + 0.58 * protein >= 12, "immune_support_lower")
    m.addConstr(-8 * vitamin_d + 10 * protein >= 0, "vitamin_d_protein_relation")
    m.addConstr(2.3 * vitamin_d + 0.28 * protein <= 55, "muscle_growth_upper")
    m.addConstr(0.95 * vitamin_d + 1.94 * protein <= 60, "energy_stability_upper")
    m.addConstr(2.92 * vitamin_d + 0.58 * protein <= 44, "immune_support_upper")


    # Optimize model
    m.optimize()

    # Print solution
    if m.status == GRB.OPTIMAL:
        print(f"Optimal objective value: {m.objVal}")
        print(f"Milligrams of Vitamin D: {vitamin_d.x}")
        print(f"Grams of Protein: {protein.x}")
    elif m.status == GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status: {m.status}")


except gp.GurobiError as e:
    print(f"Error code {e.errno}: {e}")

except AttributeError:
    print('Encountered an attribute error')
```
