The provided constraints include some redundancies (e.g., setting a maximum value twice). We've consolidated these in the code below.  The problem is formulated as a linear program with a maximization objective.  The variables represent the milligrams of vitamin B12 (integer) and vitamin B3 (continuous).  The constraints represent the limits on the various indices and a relationship between the two vitamin quantities.

```python
import gurobipy as gp

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

    # Create variables
    x0 = m.addVar(vtype=gp.GRB.INTEGER, name="milligrams of vitamin B12")
    x1 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="milligrams of vitamin B3")

    # Set objective function
    m.setObjective(5.84 * x0 + 1.76 * x1, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(3 * x0 + 7 * x1 >= 13, "digestive_support_lower")
    m.addConstr(4 * x0 + 4 * x1 >= 3, "cardiovascular_support_lower")
    m.addConstr(5 * x0 + 9 * x1 >= 13, "cognitive_performance_lower")
    m.addConstr(5 * x0 - 9 * x1 >= 0, "vitamin_relationship")

    m.addConstr(3 * x0 + 7 * x1 <= 55, "digestive_support_upper")
    m.addConstr(4 * x0 + 4 * x1 <= 14, "cardiovascular_support_upper")
    m.addConstr(5 * x0 + 9 * x1 <= 15, "cognitive_performance_upper")


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('milligrams of vitamin B12: %g' % x0.x)
        print('milligrams of vitamin B3: %g' % x1.x)
    elif m.status == gp.GRB.INFEASIBLE:
        print('The model is infeasible.')
    else:
        print('Optimization ended with status %d' % m.status)


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

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