The provided constraints contain some redundant and conflicting information. For example, there are multiple constraints on the total combined digestive support index and energy stability index, some specifying minimums and others maximums, which could make the problem infeasible.  We've attempted to consolidate and resolve these conflicts in the code below, prioritizing the more restrictive constraints where applicable.  If the problem remains infeasible, you'll need to review and adjust the constraints.

```python
import gurobipy as gp

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

    # Create variables
    b2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="B2")
    c = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="C")
    b7 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="B7")

    # Set objective function
    m.setObjective(9.96 * b2 + 9.49 * c + 2.05 * b7, gp.GRB.MINIMIZE)

    # Add constraints based on provided resource attributes
    kidney_support = {'B2': 4, 'C': 8, 'B7': 14}
    immune_support = {'B2': 9, 'C': 10, 'B7': 3}
    digestive_support = {'B2': 1, 'C': 8, 'B7': 4}
    energy_stability = {'B2': 7, 'C': 2, 'B7': 14}

    # Kidney support constraints
    m.addConstr(kidney_support['B2'] * b2 + kidney_support['C'] * c >= 16)
    m.addConstr(kidney_support['B2'] * b2 + kidney_support['B7'] * b7 >= 28)
    m.addConstr(kidney_support['B2'] * b2 + kidney_support['C'] * c + kidney_support['B7'] * b7 >= 28) # Consolidated

    # Immune support constraints
    m.addConstr(immune_support['C'] * c + immune_support['B7'] * b7 >= 20)
    m.addConstr(immune_support['B2'] * b2 + immune_support['B7'] * b7 >= 31)
    m.addConstr(immune_support['B2'] * b2 + immune_support['C'] * c + immune_support['B7'] * b7 >= 31) # Consolidated

    # Digestive support constraints (Resolved potential conflicts by taking the most restrictive constraints)
    m.addConstr(digestive_support['B2'] * b2 + digestive_support['B7'] * b7 >= 41)
    m.addConstr(digestive_support['B2'] * b2 + digestive_support['C'] * c >= 45)
    m.addConstr(digestive_support['B2'] * b2 + digestive_support['C'] * c <= 45) # Added upper bound
    m.addConstr(digestive_support['B2'] * b2 + digestive_support['B7'] * b7 <= 117) # Added upper bound
    m.addConstr(digestive_support['B2'] * b2 + digestive_support['C'] * c + digestive_support['B7'] * b7 >= 24)


    # Energy stability constraints (Resolved potential conflicts by taking the most restrictive constraints)
    m.addConstr(energy_stability['B2'] * b2 + energy_stability['C'] * c >= 16)
    m.addConstr(energy_stability['B2'] * b2 + energy_stability['B7'] * b7 >= 16)
    m.addConstr(energy_stability['B2'] * b2 + energy_stability['B7'] * b7 <= 25) # Added upper bound
    m.addConstr(energy_stability['B2'] * b2 + energy_stability['C'] * c + energy_stability['B7'] * b7 >= 16)

    # Additional constraints
    m.addConstr(-6 * b2 + c >= 0)
    m.addConstr(7 * c - 10 * b7 >= 0)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('B2:', b2.x)
        print('C:', c.x)
        print('B7:', b7.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')
```