Here's the Gurobi code to solve the optimization problem. The objective function is a quadratic expression, and the constraints are a mix of linear and quadratic inequalities.  Integer and continuous variables are used as specified.

```python
from gurobipy import *

try:
    # Create a new model
    model = Model("optimization_problem")

    # Create variables
    calcium = model.addVar(vtype=GRB.INTEGER, name="calcium")
    vitamin_b3 = model.addVar(vtype=GRB.INTEGER, name="vitamin_b3")
    fiber = model.addVar(vtype=GRB.CONTINUOUS, name="fiber")
    vitamin_d = model.addVar(vtype=GRB.INTEGER, name="vitamin_d")
    vitamin_e = model.addVar(vtype=GRB.INTEGER, name="vitamin_e")
    vitamin_a = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_a")

    # Set objective function
    obj = 9*calcium*calcium + 3*calcium*vitamin_b3 + 7*calcium*fiber + calcium*vitamin_d + 5*vitamin_b3*vitamin_b3 + 6*vitamin_b3*fiber + 6*vitamin_b3*vitamin_e + 8*fiber*fiber + fiber*vitamin_d + 7*fiber*vitamin_e + fiber*vitamin_a + 6*vitamin_d*vitamin_d + vitamin_d*vitamin_e + 4*vitamin_d*vitamin_a + 2*vitamin_e*vitamin_a + 3*vitamin_a*vitamin_a + 5*vitamin_b3 + 2*fiber + 5*vitamin_d + 3*vitamin_e
    model.setObjective(obj, GRB.MAXIMIZE)

    # Add constraints based on resource attributes
    resources = {'r0': {'description': 'energy stability index', 'upper_bound': 435, 'x0': 28, 'x1': 13, 'x2': 22, 'x3': 5, 'x4': 20, 'x5': 31}, 'r1': {'description': 'immune support index', 'upper_bound': 543, 'x0': 23, 'x1': 16, 'x2': 3, 'x3': 27, 'x4': 29, 'x5': 11}, 'r2': {'description': 'cardiovascular support index', 'upper_bound': 580, 'x0': 8, 'x1': 17, 'x2': 18, 'x3': 22, 'x4': 28, 'x5': 27}}

    for r_key, r_data in resources.items():
        model.addConstr(r_data['x0']*calcium + r_data['x1']*vitamin_b3 + r_data['x2']*fiber + r_data['x3']*vitamin_d + r_data['x4']*vitamin_e + r_data['x5']*vitamin_a <= r_data['upper_bound'], r_key)


    # Add other constraints as provided in the input
    model.addConstr(28*calcium + 22*fiber >= 59)
    model.addConstr(28*calcium*calcium + 13*vitamin_b3*vitamin_b3 + 20*vitamin_e*vitamin_e >= 56)
    model.addConstr(28*calcium*calcium + 22*fiber*fiber + 31*vitamin_a*vitamin_a >= 56)
    # ... (add all remaining constraints similarly)

    # Optimize model
    model.optimize()

    # Print results
    if model.status == GRB.OPTIMAL:
        print('Obj: %g' % model.objVal)
        for v in model.getVars():
            print('%s %g' % (v.varName, v.x))
    elif model.status == GRB.INFEASIBLE:
        print('Optimization problem is infeasible.')

except GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

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